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AI for Sales Forecasting & Sales Process Execution How artificial intelligence provides more direction for forecasting and more wins
April 2017
In this issue
Introduction 2
How “Not” to Forecast 3
How to Create & Manage Your Forecast ... So You Can Trust It 4
6 Tenets of an Accurate Sales Forecast 5
Research From Gartner: Market Guide for SaaS-Based Predictive Analytics Applications for B2B 7
2
Introduction
“You missed the number? Hey, no problem, you’ll
get it next quarter!” said no CEO or board member
ever!!!
Credibility in hitting the number is absolutely the
most important thing for a sales leader. And
these leaders that hit their number regularly,
don’t have it easy. It’s usually a grind with a mad
scramble at the end of the quarter to hit plus or
minus 5%. It doesn’t have to be such a painful
process.
Artificial intelligence for sales forecasting builds
trust. If you can trust what you see in the
pipeline and that your pipeline is filling at the
appropriate rate with qualified leads and
opportunities that fit your ideal customer profile,
you can have confidence that you’ll hit your
number.
Before I tell you what’s needed to have a forecast you
can trust – let’s look at how we create and manage a
forecast today.
3
How “Not” to Forecast
If you’re like most execs in sales or sales ops, you’re
armed only with a couple of reports from your
CRM and a spreadsheet. You list and manage the
opportunities by sales rep and by stage - then you
spend a lot of time in conversations with your sales
team. Eventually you contact them daily for status
updates. Conversations are great with the sales
team – especially on strategy to close deals, but it’s
such a waste of valuable time to do daily status calls
between manager and sales reps on the same deals.
You still end up with minimal visibility and knowledge
about deals. And this leaves very little time for selling,
coaching and strategizing – among other priorities.
When you print out the pipeline for the team – it
shows 3x to 4x the number. Questions you should ask
yourself. Can I rely on that 3x to 4x pipeline number?
How do I find out how good that pipeline really is? Can
I rely on my weighted average number? Is there an
easier way to get visibility into deals? What do I have to
do to get this forecast to where I can trust it?
Additional side effects that your current sales
forecasting process causes:
Because the current sales forecasting process is such
a time suck, you hardly have any time left for:
1. Coaching and developing the sales team
2. Work on maximizing conversions throughout each stage
3. Ramping new sales reps
4. Understanding the effects of my lead-gen effort
And by not spending enough time on these four things,
closing business and quota attainment suffers.
4
How to Create & Manage Your Forecast ...So You Can Trust It
Sales Automation for the Sales Forecast
Imagine if you had an expert coach that knew your sales
process and prompted you on a logical next step on a
deal that was still active but you were starting to neglect?
Or if all of your deals magically went to the appropriate
stage and milestone and gave you an accurate forecast.
These are examples of what automation can provide.
Automation is valuable because it saves a ton of time,
and it organizes and applies discipline and rigor without
the manual effort. With process, workflow, and pipeline
hygiene automation, an entire sales team will have a
lot more time for selling and more value-added activity.
These efforts are normally manual, time consuming and
error prone.
The Use of Artificial Intelligence
A.I. provides an order of magnitude of valuable
information and insights for sales forecasting. By
applying the disciplines of sales automation for sales
forecasting, data can be analyzed and insights derived.
For example, insights can alert you to whether you have
enough pipeline to hit the number this quarter or next
quarter, and it can tell you what to do about it, such as
how much to increase Average Selling Price, or increase
lead gen efforts or conversion rates.
Do not use BI tools for sales analysis!
Leave the Business Intelligence tools for accounting,
HR or other departments where the data is more
manageable. Using BI against untamed data in the CRM
is almost as bad as using the spreadsheet. BI tools in the
sales department usually end up as great looking graphs
displaying insufficient and inaccurate data regurgitated
from the CRM. These tools lack the automation of a
sales forecasting application and the artificial intelligence
to keep the pipeline with clean hygiene, realistic deals
and accuracy.
5
6 Tenets of an Accurate Sales Forecast
So, how do you apply sales automation and AI to your sales forecast?
The following are 6 strategies that can be utilized
through automation and A.I., that will enhance the
sales forecast as well as provide additional benefits to
increase win rates and quota attainment.
1. Prescribe rules for the sales process
Create rules for advancing and regressing leads and
opportunities as well as when and why to close them
as a loss. Keep everyone from management to the
sales rep on the same page about process rules.
Discipline around when to move a deal, why, and
where it belongs will keep things well organized and
consistent. Here is a great resource on how to build,
tweak or overhaul your sales process - How to Build a
Winning Sales Process Guidebook
2. Enforce the sales process
CSO Insights research shows a 23% increase in quota
attainment occurs when a rigorous sales process is
used. By understanding how a deal flows through the
funnel you’ll have accurate information on where deals
get stuck and conversion rates. This is important data-
driven coaching information. And it’s not enough to
just have a process. Many do, but it’s just written down
somewhere or a powerpoint print out is pinned up on a
cubical wall and therefore loosely adhered to.
6
There needs to be reminders to the sales reps on the
next step that follows the appropriate sales process.
These steps have a time limit if done effectively.
Automation serves sales reps in this way - increasing
the likelihood of a sale.
3. Enforce pipeline hygiene
The bane of every sales manager and sales rep is
the current state of pipeline analytics. There are too
many deals that are not accurate, not up to date, in
the wrong stage or just don’t belong in the pipeline.
You don’t have to spend the hours scrutinizing deals
for hygiene or increase the coverage of pipeline to
compensate - you just need to have the disciplines of
hygiene to be aware of better quality deals with higher
probability of wins. You can solve this problem by
reading further in this document.
4. Make it easy to update the CRM
Sales reps need to provide their point of view on deals.
These status updates need to be in the CRM as soon
as possible after any meeting or communication with
prospects and customers.
5. Capture “the right” signals for Artificial Intelligence
Artificial intelligence can provide great insights,
but false signals could distort prescribed insights
for decisions. Quality signals such as emails from
prospects and meetings from the sales teams’
calendars are helpful to determine opportunity quality.
6. Analyze how deals flow through the funnel by rep and by time
Now that you have 1 through 5 of the strategies in
order, managers and sales reps have sufficient and
accurate data amplifying their ability to get real
insights. This guidebook explains how to analyze
performance and customize coaching to get the entire
team exceeding quota.
Summary
By applying automation and AI in this way, you’ll be
able to trust your sales forecast, while developing and
making your sales team better. No longer will you
wonder if the 3x or 4x pipeline coverage are really
comprised of good deals. Conversations that you are
having with the sales reps will make their way into
the CRM for better analysis and decisions. Ramp
time of sales reps will be quicker, providing faster
times to quota attainment. And you’ll have time along
with data-driven analytics to better coach the team.
And thank goodness - you can finally get rid of that
spreadsheet!
To learn how our customers are benefitting from these
strategies - click here.
To see how sales automation and artificial intelligence
can be applied to give you a consistent sales process
and accurate forecast with TopOPPS - request a demo.
Source: TopOPPS
7
Research From Gartner
Market Guide for SaaS-Based Predictive Analytics Applications for B2B
SaaS-based predictive analytics applications are
helping B2B salespeople and marketers improve win
rates, deal velocity and size. IT application leaders
must understand the dynamics of this emerging and
rapidly growing category and identify providers that
can support these business units.
Key Findings
■ The market for SaaS-based predictive analytics
applications is still small and nascent (Gartner
estimates its worth at $100 million to $150 million
by the end of 2016), but it offers a compelling ROI
potential that should lead to rapid growth within
the next two years.
■ Many vendors, particularly those that target
marketing (rather than sales) users, now offer broad
solution suites that address many different use
cases — from segmentation to account selection,
demand generation and upsell/cross-sell.
■ Applications are typically purchased using short-
term subscription contracts (two years or less),
and vendor churn at the end of contracts remains
high due to unrealized expectations and/or the
ease of switching.
■ While differentiation exists based on focus, go-to-
market strategy, integrations, functionality and/or
data sources, many vendors use similar messaging
and positioning, which causes confusion for buyers.
8
Recommendations
IT application leaders who support marketing and sales:
■ Help your marketing team by investigating
and adopting SaaS-based predictive analytics
applications to improve segmentation, account
selection, demand generation and lead scoring
to increase conversion rates and contributions to
pipeline and revenue.
■ Help your sales team by investigating and using
SaaS-based predictive analytics applications to
improve forecasting, pipeline management and
upselling/cross-selling to increase win rates, deal
velocity and average sales price.
■ Do not make purchase decisions solely on model
performance during proofs of concept; also
consider factors such as data sources, integration
options, industry expertise, customer references
and overall customer experience.
Market Definition
This document was revised on 4 October 2016. The
document you are viewing is the corrected version. For
more information, see the Corrections page on
gartner.com.
This market encompasses an emerging category
of effectiveness and productivity applications for
B2B sales and marketing professionals. Software
as a service (SaaS)-based applications are used at
different points of the sales funnel for both prospects
and existing customers. While traditional CRM lead
management and sales force automation (SFA) offer
some functionality to help marketers and salespeople
make more effective decisions and are starting to
incorporate artificial intelligence (AI) and machine
learning, most of the analytics they incorporate
are based on predefined rules and diagnostic and
descriptive analytics.
The solutions in this market leverage a range of predictive and, in some cases, prescriptive analytics techniques and models to enable better decision making based on a combination of internal and external data at both account and contact levels. They also use machine-learning techniques to improve accuracy over time as more data is added to the model.
The market includes two discrete types of solutions. One set of applications is typically used by marketers (or, in some cases, sales development reps [SDRs]) and covers a set of use cases higher up in the sales funnel). The use cases are shown in Figure 1. The models include both fit (propensity to buy) and intent (likelihood that a company is actively looking). A combination of first-party internal data from CRM lead management and SFA systems, along with third-party external data from the web, proprietary and public databases, is used to build the models.
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Source: Gartner (September 2016)
Figure 1. Predictive B2B Marketing Use Cases
The other set of applications is typically used by those in sales roles, including sales leadership, frontline sales managers, sales reps, SDRs and sales operations leaders. They include a set of use cases for the middle and latter part of the funnel, as well as with existing customers. These use cases are shown in Figure 2. While some external third-party data may feed the model, the models for predictive sales applications rely more heavily on first-party data from SFA systems, as well as emails and calendar appointments. In some cases (particularly for upsell/cross-sell),
data from ERP systems and data warehouses are included in the model.
Many vendors that sell solutions to cover the marketing use cases also provide models for upsell/cross-sell identification. And while demand generation models can provide accounts and contacts for use in CRM lead management systems, SDRs and sales reps can use those same models for prospecting. In addition, Gartner expects to see vendors moving down or up the funnel to cover additional use cases, so both types of solutions are being included in a single Market Guide. Buying processes for these types of solutions are often led by IT
10
Source: Gartner (September 2016)
Figure 2. Predictive Sales Use Cases
or analytics teams, particularly for larger enterprises or those outside of high-tech companies. But since the solutions are all SaaS-based, many of the buying processes for emerging and high-growth technology providers are led by demand generation/marketing operations and/or sales operations, with IT playing an advisory or supporting role.
Predictive applications designed to improve renewal rates and optimize pricing are treated as discrete markets by Gartner and are no longer included in this guide. While SFA vendors have some opportunity scoring capabilities, that is not the primary purpose of the applications, so they are not included. Vendors that offer predictive B2B marketing or sales functionality as part of a service rather as a stand-alone product (including ServiceSource and Revana) or through a data or advertising platform (including Madison Logic) or those that only very recently added predictive capabilities to their platforms (including Avention) are not included. Finally, solutions that use
rule-based approaches (but not data science techniques) are not included.
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Market Direction
The market has grown and matured since the
introduction of the last Market Guide in early 2015,
but it still exhibits the characteristic of a fast-growing,
yet immature, early-stage market. Adoption has
largely occurred from larger or high-growth technology
providers in the U.S. However, both predictive B2B
marketing analytics and predictive sales analytics
have been included in Gartner Hype Cycles for the
last two years. They both have “high” benefit ratings,
“emerging” maturity ratings and market penetration
rates of 5% to 20% of their target audiences.
Predictive B2B marketing analytics is at the Peak of
Inflated Expectations, while predictive sales analytics
is positioned at the beginning of the Trough of
Disillusionment. Given the characterization of the
market, adopters may see significant benefit but also
experience the trial and error and potential need to
switch vendors that come with this type of market.
Gartner estimates that the market will see $100 million to $150 million in vendor revenue by the end of 2016. Despite the market’s comparatively small size, Gartner believes the aggressive positioning in the Hype Cycles is justified because of the high growth potential (both outside the U.S. and in other B2B industries) and the compelling ROI that clients have achieved using these types of solutions. The market has exhibited signs of maturity (especially in the high-tech industry in the U.S.), and vendors in the space have improved their solutions (as well as the customer experience), allowing buyers to move past the pilot or proof-of-concept stage and roll out the applications more broadly. As a result, buzz has increased, and Gartner has seen a noticeable uptick in client inquiries, including from clients in financial services, life sciences, business services and other industries.
Several trends have led to increased demand for solutions. First, account-based marketing (ABM) has emerged as a key investment area for many B2B companies, and predictive analytics can improve both account selection and demand generation elements. Many predictive B2B marketing analytics providers were
quick to position their solutions as key enablers of ABM.
Next, forecasting and pipeline management have become
more challenging for many B2B sales leaders as buyers
exert ever more control over their buying processes.
Many leading SFA tools are the system of record for
forecasting and thus provide only basic forecasting
capabilities. Sales operations teams often have to
spend hours managing forecasts in Excel or business
intelligence (BI) tools on a regular basis. Predictive sales
analytics applications not only provide huge productivity
boosts by automating this largely manual task, but
also provide greater accuracy and visibility around the
expected outcome of individual deals, as well as the
likelihood of meeting forecast targets.
Finally, both lead management and SaaS SFA
applications have reached mainstream adoption, with
the former at 20% to 50% adoption and the latter at
more than 50% adoption in the 2016 Hype Cycle for
CRM sales. Many adopters of predictive analytics have
used one or both of those solutions for three to five (or
more) and have the capacity and desire to take on new
projects. With predictive solutions starting at $25,000
per year, many of the more sophisticated B2B
marketing and sales operations leaders have started
to look at predictive analytics as a potential answer to
some of their more vexing problems.
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All the vendors in this guide are privately held and
are either venture-backed or “bootstrapped” with
capital from their own founders. There has been some
consolidation since the last Market Guide, with smaller
vendors exiting the market. For example, Fliptop was
acquired by LinkedIn, and SalesPredict was purchased
by eBay, in both cases for their machine-learning
capabilities, and InsideSales.com bought C9. However,
new entrants have more than made up for those exits.
Gartner does not expect to see a new market entrant
that solely addresses the current use cases through
2017 (at least in North America) but does expect
more traditional vendors, such as Oracle or Adobe,
to enter through acquisition of one of the existing
predictive B2B marketing vendors. Salesforce and
Microsoft have acquired AI companies and may also
choose to make a purchase or investment to round out
their existing capabilities (although both have equity
investments in at least one vendor in this guide). Most
predictive vendors were able to raise money before
the venture capital downturn in late 2015 and early
2016, and some are also aiming for cash-flow break-
even in the next six to 12 months. Nevertheless, the
market (especially inside the U.S.) is crowded, and one
or more vendors in this guide may find it difficult to
survive as an independent company.
With the need to become profitable and the burden
of acquiring local data, most North America-based
vendors have focused close to home and shied away
from international expansion, at least in terms of
targeting companies outside North America or hiring
salespeople in other regions. (They do support
international sales and marketing teams from North
America-based customers.) A few vendors included in
this guide are based in the U.K. or France, and they
expect to target Germany and other Western European
countries in the next year. No vendors report targeting
Australia, Asia or Latin America in any meaningful way.
For the marketing use cases, data can be an issue in
certain countries, particularly those with double-byte
character sets. (Fuzzy logic matching is the most
problematic in Asia.)
Since the last Market Guide, the predictive models
have moved from being predominantly “black box,”
where the signals that drive the models are hidden,
to being more open and transparent, which Gartner
believes is a positive step. However, IT and sales and
marketing leaders need to be careful to figure out
what signals they want to expose to sales reps within
the account, opportunity and lead objects in the SFA
system. It is crucial to find the right mix between
providing enough information to build trust and
making the data more actionable versus providing too
much and confusing the rep or SDR.
Differentiation remains an issue for most vendors
discussed here. On the predictive B2B marketing side,
most vendors utilize similar data science techniques,
create models that can self-tune, support the same
use cases, source similar third-party data (including
intent data from Bombora and others) and offer
rapid turnaround (or self-service) model creation. The
model creation time used to be a differentiator, but
that has largely been erased. The lack of apparent
differentiation and the typically short contracts (12 to
18 months is common) have made it easy for vendors
to poach customers away at the end of their contracts.
On the predictive sales side, similar differentiation
issues exist, especially because external data is
less important. There are some more clear points
13
of differentiation across both marketing and sales,
and they are called out as part of the sample vendor
write-ups. While model accuracy in a proof of concept
is an important factor, Gartner recommends that IT
application leaders who support sales or marketing
(and anyone in the buying process) also consider a
wide range of factors, including focus, integrations,
customer references and product vision, when making
recommendations or decisions.
Market Analysis
More than 20 vendors offer SaaS-based predictive
analytics applications specifically for use by B2B
sales and marketing professionals. While outbound
SDRs (and some sales reps) get engaged at the top
of the sales funnel in a prospecting capacity (through
emails or phone calls), most predictive use cases at
the top of the funnel are the domain of marketing
professionals, including those in demand generation,
marketing operations and product marketing.
Here is more information about the different types of
predictive B2B marketing Use Cases:
■ Total addressable market (TAM) identification —
B2B companies often want to understand how big
of an opportunity exists before entering a market
or making staffing and investment decisions. While
a TAM number may exist, not all companies in
the market are easily addressable. The predictive
models can identify the size of the market (both in
revenue and number of accounts) for which their
solutions would address and the total roll-up of
all companies in a market with a fit score above a
certain level. Sales operations leaders can also use
this data for territory planning purposes.
■ Vendors: EverString, GrowthIntel, Infer, Mintigo,
MRP, Radius
■ Segmentation — Predictive models can be used
to create segments of accounts based on signals
(fit or intent) rather than traditional firmographics.
These groups of accounts can be the basis for
campaigns in lead management systems or
segment-based ABM programs. As predictive
signals change, the segments change with them.
■ Vendors: 6sense, BrightTarget, Datanyze,
EverString, GrowthIntel, Infer, Lattice Engines,
Leadspace, Mintigo, MRP, Radius, SalesChoice
■ Account selection — One of the fastest-growing
predictive use cases is to identify the best
accounts to select for an ABM program. Marketers
use predictive models to highlight anywhere from
a few dozen to more than a thousand accounts
and tier them based on propensity to buy (fit,
intent or both). The accounts are then exported for
campaign orchestration to lead management, web
personalization and advertising platforms.
■ Vendors: 6sense, BrightTarget, Datanyze,
EverString, Infer, InsideSales.com, Lattice Engines,
Leadspace, MarianaIQ, Mintigo, MRP, Radius,
SalesChoice
■ Demand generation — While some B2B
organizations (particularly those with subscription-
based offerings, free trials and freemium solutions)
are blessed with more inbound leads than they
can effectively manage, most are not. Marketers,
SDRs and sales reps (both generally and as part of
account-based programs) are constantly looking
14
to expand the people to whom they prospect and
have turned to predictive-driven solutions instead
of traditional lists. Vendors offer predictive models
to identify companies based on fit and intent and
then deliver contacts that can be exported to lead
management or SFA systems.
■ Vendors: 6sense, Datanyze, EverString,
GrowthIntel, IKO System, Infer, Lattice Engines,
Leadspace, MarianaIQ, Mintigo, MRP, Radius
■ Lead scoring — Predictive lead scoring was the
initial marketing use case and far away the most
mature one. Traditional lead scoring is based on
two dimensions (demographic/firmographic and
engagement), while predictive lead scoring makes
use of more (and more relevant) signals that are
correlated with propensity to buy to go along with
engagement and/or intent. These models have
generally proven to be far more accurate than
traditional lead scoring at predicting the likelihood
of a lead converting into an opportunity and
closing.
■ Vendors: 6sense, BRIDGEi2i, BrightTarget,
Datanyze, DxContinuum, EverString, Infer,
InsideSales.com, Lattice Engines, Leadspace,
Mintigo, MRP, Radius
Fewer vendors offer solutions to address sales
rather than marketing use cases, although many of
the marketing vendors do provide upsell/cross-sell
models, which share similarities with other solutions
they offer. But while the marketing models rely heavily
on external data, the sales models are more reliant on
internal data.
■ Forecasting — As the system of record, traditional
SFA tools often lack the forecasting and pipeline
management capabilities required by sales
operations leaders, while the data going into
the forecasts (typically entered by the sales rep)
often lacks the rigor and accuracy that sales
organizations require. Predictive forecasting
models solve both problems by automating
the forecasting and pipeline management
processes and using data science models to score
opportunities and roll them up at various levels.
Sales leaders and managers can see the forecast
revenue at product, team or geographic levels,
while reps can gain better insight for their own
opportunities and quota attainment.
■ Vendors: Aviso, BRIDGEi2i, BrightTarget, Clari,
DxContinuum, InsideSales.com, SalesChoice,
TopOPPS
■ Opportunity scoring — Predictive forecasting has
replaced the need for stand-alone opportunity
scoring for many B2B companies, but there are
still situations where opportunity scoring can be
helpful for both sales reps and their managers.
Understanding the true likelihood of close (and the
close date) instead of going off what the rep has
entered alone can help dictate focus and attention.
Vendors are also moving toward giving prescriptive
guidance and coaching (also true with the
forecasting solutions) to help reps and managers
understand how to improve the likelihood of
closing a deal.
■ Vendors: BRIDGEi2i, Clari, DxContinuum, Infer,
InsideSales.com, SalesChoice, TopOPPS
15
■ Upsell/cross-sell — Many larger and more
established companies add far more revenue from
growing existing accounts versus signing new
ones, so predictive upsell and cross-sell models
have been around for longer than most other use
cases. The applications primarily rely on internal
data, but not all of it is in CRM systems. Models
that incorporate transactional data from order
management systems and data warehouses
are usually more accurate. Some vendors have
extraction, transformation and loading (ETL) tools
or various data layers to get at that data, while
others need the customer to provide extracts. The
models provide not only the accounts to target, but
also the solutions to offer. Some B2B companies
build their own systems at first, but the care and
feeding of the system often drive a switch to a
third-party solution.
■ Vendors: 6sense, BRIDGEi2i, BrightTarget,
DxContinuum, Entytle, EverString, Infer, Lattice
Engines, Mintigo, Radius, SalesChoice
Representative Vendors
The vendors listed in this Market Guide do not imply an
exhaustive list. This section is intended to provide more
understanding of the market and its offerings.
Note: The “high-tech industry” includes technology
vendors (hardware and software), service providers and
communications service providers (CSPs).
6sense
www.6sense.com
Use Cases: Segmentation, account selection, demand
generation, lead scoring, upsell/cross-sell
San Francisco-based 6sense was created in 2013 and
has raised $46 million in venture funding. It targets
marketers and supply sales leaders with outbound
prospecting tools that identify when buyers are in-
market, helping them answer the answer of “timing.”
It also offers lead scoring and upsell/cross-sell
predictions. While its customers certainly leverage
6sense’s account and contact/lead fit-based models,
6sense has invested more heavily in time-based, intent
modeling techniques than any other vendor in this
guide. 6sense’s patented methodology predicts when
prospects are in an active buying cycle and where
the prospect is in his or her buying journey. It has
built a private data network that includes publishers,
search engines, blogs, community forums and many
other sources (and augments data from other intent
providers). 6sense also utilizes IP to company matching
and cookie syncing and incorporates time-based and
relativity-based predictions into its models to gauge
intent before someone fills out a form or “raises a
hand.” 6sense leverages its publisher relationships
to allow marketers to reach their buyers through
their ABM efforts. The time-based intent modeling
capabilities feature prominently into the company’s
positioning and messaging and customer testimonials.
6sense recently introduced a lower entry price to appeal
to midmarket companies, but it has historically targeted
large enterprises in high-tech and other verticals. The
lower entry pricing and shorter-term contracts now
16
make it easier for customers to test and buy 6sense’s
solutions. 6sense is often a good fit for companies that
are in highly competitive markets, where understanding
buyer readiness (timing) is critical. 6sense was named a
Gartner Cool Vendor in 2015.
Industries Represented: Financial services, high tech,
manufacturing, medical devices, professional services
Supported Integrations: Bombora, Forbes (and other
large publishers), Integrate, Madison Logic, Marketo,
Oracle (BlueKai and Eloqua), Salesforce
Notable Customers: BlueJeans Network, Dell,
GE, NetSuite
Pricing: Starts at $50,000 per year for midmarket
companies (higher for larger companies) and
increases based on the number of products that are
being modeled
Aviso
www.aviso.com
Use Cases: Forecasting
Menlo Park, California-based Aviso has been in business
since 2012 but released its first predictive forecasting
solution in early 2015. It has 65 employees and has
raised $23 million to support its efforts. The company
creates an integrated forecasting view across all
revenue sources that is completely consistent from
the global level down to the individual BU, region or
rep. This is built on top of a forecasting engine that
utilizes predictive and prescriptive analytics models
to determine the likelihood of a deal closing, the
date it will close and how much it will be worth. Aviso
provides capabilities to help sales operations and
sales leadership get early warnings and easily see the
discrepancies between traditional and Aviso forecasts
and dynamically highlights the recent changes that have
impacted its models. Aviso’s architecture has the ability
to incorporate multiple data sources, including CRM,
email and calendar data, in its models. One of the
capabilities it believes to be unique is modeling around
billings, revenue and pipeline, instead of just bookings
data (to better predict the actual size of the deal).
Aviso also provides automated alerts when its models
indicate changes, such as a deal being likely to slip.
Aviso focuses on companies with more than 50 sales
reps. While it supports other SFA systems, a large
fraction of the company’s clients use Salesforce.
Aviso has been in this market for less than two years
but already has more than 40 customers. It is a good
fit for midmarket and enterprise customers across
several industries. Aviso is able to create forecast
models against multiple SFA systems simultaneously,
which can allow customers not to have to rush to
integrate SFA systems after making an acquisition.
Industries Represented: High tech, manufacturing,
media, professional services
Supported Integrations: Gmail, Microsoft Dynamics,
Microsoft Exchange, NetSuite, Oracle, Salesforce, SAP
Notable Customers: Hewlett Packard Enterprise,
Marketo, Nutanix, Splunk
Pricing: Aviso has several editions of the product, but
it starts at $900 per user per year, with a $20,000-per-
year minimum spend.
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BRIDGEi2i
www.bridgei2i.com
Use Cases: Lead scoring, opportunity scoring,
forecasting, upsell/cross-sell
Bangalore, India-based BRIDGEi2i is the only
vendor in this guide headquartered outside North
America, the U.K. or France. The company started
as a predictive analytics consulting firm in 2011
and rolled out its first product in 2014. Most of its
customers are in the U.S., but it also has some in
Asia (in India, in particular). BRIDGEi2i offers stand-
alone opportunity scoring, as well as a predictive
forecasting solution. The solutions leverage Monte
Carlo simulations, and both managers and reps can
do scenario modeling and what-if planning in their
native Force.com application or through a third-party
visualization tool. Reps can also benchmark their
expected performance against those of peers. For
sales leaders and sales operations leaders, BRIDGEi2i
can offer recommendations to help them act on the
data. The company also offers a stand-alone upsell/
cross-sell model and a lead scoring solution, based on
fit and intent (although it leverages fewer external data
sources than most other solutions in this guide).
BRIDGEi2i targets large companies in a variety of B2B
industries (it has 10 customers in the Fortune 100
alone) with solutions that are more custom-designed
rather than off the shelf. It most commonly replaces
homegrown solutions. Many of its customers have
internal data scientists; BRIDGEi2i’s professional
services team works closely with them around
potential solutions. Although some of its solutions
are immature and lacking in functionality when
compared with competitors’, the professional services
capabilities can often fill in the gaps. BRIDGEi2i’s
flexibility and common data model make it a fit for
very large companies that want an alternative to more
packaged options from other vendors.
Industries Represented: Consumer packaged goods
(CPG), financial services, high tech (manufacturing),
insurance, retail
Supported Integrations: Marketo, Salesforce
Notable Customers: Not indicated.
Pricing: Starts at $50,000 per year and increases
based on customizations, number of products, scope,
models and sales team size
BrightTarget
www.brighttarget.com
Use Cases: Segmentation, account selection, lead
scoring, forecasting, upsell/cross-sell
West Midlands, U.K.-based BrightTarget started as an
innovation department within a BI/data consultancy
in 2012 and was established as a separate business
in 2014. It was known as Kairos until late last year,
when it sold off the consulting firm. While BrightTarget
sells to high-tech companies in the U.K., it focuses
on industries that others have paid less attention to,
including building services and media. The dataset it
builds also reflects these priorities as the BrightTarget
Business Index includes data on small building
companies. BrightTarget also differentiates by taking
a customer lifetime value (CLTV)-driven approach to
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its modeling, showing those values even when scoring
leads. As the market has matured, BrightTarget
has done the same thing by significantly reducing
the time to develop models and rolling out a new
lightweight forecasting tool. BrightTarget also offers
marketing attribution capabilities and will roll out TAM
identification and demand generation solutions in the
coming months.
BrightTarget remains focused on the U.K. market and
the current industries for which it has had success.
Upsell/cross-sell and account scoring (from purchased
lists) remain its entry points. The CLTV-related
capabilities address a common pain point for Gartner
clients as they look to identify the best accounts to
target for expansion. As BrightTarget rolls out more
top-of-the-funnel solutions, it will have an opportunity
to scale its business but will face greater competition,
as well.
Industries Represented: Building services, high tech,
media
Supported Integrations: Adobe Marketing Cloud,
Force24, Marketo, Oracle (Eloqua), Salesforce (Pardot
and Sales Cloud)
Notable Customers: BSS Industrial, Company Check,
Euromoney Institutional Investors, Speedy Hire
Pricing: Starts at $32,000 per year and increases
based on the number of models created and the
amount of data being used
Clari
www.clari.com
Use Cases: Forecasting, opportunity scoring
Sunnyvale, California-based Clari has been in existence
since 2013 (with a product in 2014) but traces its
predictive analytics legacy back to 2005. The company’s
founders (and much of its staff) came from machine
pioneer Clearwell, which was sold to Symantec in
2011. Clari has raised $46 million to date and delivers
predictive models for pipeline inspection, deal and
forecast management — what Clari customers call the
“opportunity to close” process. It has a wide range of
prepackaged integrations (although it supports only
Salesforce among SFA vendors). Clari was the first to
bring email and calendar data into forecast and deal
models. The company can track forecast and deal detail
changes in real time without exporting, and it features a
“graph” that prioritizes sales reps’ tasks across key deals
to drive better productivity and a “grid” that helps both
reps and managers with real-time updated deal progress.
Clari recently announced its AI-driven messaging
platform that proactively prescribes actions to sales
teams (called “nudges”) to drive behavior and actions
that increase deal velocity and close probabilities.
With a higher starting price than some of its
competitors, Clari focuses on pre-IPO, private-equity-
backed and public companies, mainly in technology,
media and professional services. Clari is a good fit for
upper-midmarket companies and enterprises that are
looking at applying predictive analytics to drive better
pipeline management and more accurate forecasting
and use Salesforce for SFA.
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Industries Represented: High tech, media,
professional services
Supported Integrations: Box, Dropbox, Evernote,
Gmail, LinkedIn, Microsoft Exchange, Salesforce
Notable Customers: Hewlett Packard Enterprise, Intel,
Juniper Networks, Palo Alto Networks
Pricing: Clari declined to provide pricing details for
this guide. Please contact the vendor directly for
pricing details.
Datanyze
www.datanyze.com
Use Cases: Segmentation, account selection, demand
generation, lead scoring
Four-year-old San Mateo, California-based Datanyze has been in the predictive space for only a year, but it has built up a strong following in other areas (more than 30,000 users) by tracking technology and mobile installs and alerting SDRs when an account has installed a particular product. (Datanyze also provides a free browser plug-in.) Datanyze evolved from a technographic information provider into a full-fledged data platform (with 45 million domains and contact information) and then added predictive models for both marketers and SDRs to better take advantage of this data. They can also use the data platform to enrich account, lead or contact information. The company currently leverages only fit models, but it has its own IP tracking capabilities so that anonymous website traffic can be easily added to them. Intent is also derived by looking at whether an account recently
added a competing or complementary product.
The predictive capabilities are packaged as an add-on
to the data platform, but the total solution price (data
and models) is still lower than most, if not all, other
predictive solutions. The company focuses exclusively
on selling to technology companies (especially
SaaS providers). While Datanyze gets a foothold in a
company through the technology tracking capabilities
being used by SDRs, the data platform solutions are
also purchased by marketers to help with demand
generation. The predictive add-on is increasingly being
purchased, especially for top-of-the funnel processes.
Datanyze has raised only $2 million in venture funding
(in a 2014 seed round). The company is a fit for
emerging SaaS companies looking for cost-effective
predictive demand generation solutions. Datanyze was
named a Gartner Cool Vendor in 2016.
Industries Represented: High tech (primarily SaaS)
Supported Integrations: HubSpot, Marketo, Salesforce
Notable Customers: HubSpot, Marketo, Namely,
New Relic
Pricing: The data management solution with predictive
add-on starts at $20,000 per year and increases as a
result of customizations and additional model creation.
DxContinuum
www.dxcontinuum.com
Use Cases: Lead scoring, opportunity scoring,
forecasting, upsell/cross-sell
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Fremont, California-based DxContinuum is one of
the newer players in the space (with a product since
2014 and funded in 2015) and takes one of the
more unique approaches to its targeting and product
portfolio strategies. The company mainly targets sales
operations and sales leadership with an upsell/cross-
sell solution then adds on an opportunity scoring
solution that rolls up into a predictive forecast. It
also offers a lead scoring solution. DxContinuum
typically deals with more complex sales processes,
and it offers a data preparation layer to transform
data to more easily be used in a model. The models
it creates are highly sensitive to changes in data and
how often that data changes. DxContinuum has several
unique capabilities, including the creation of a family
of models with different regression techniques for a
single use case (selecting the one that produces the
best fit) and even the ability to allow a customer to
run the solution on-premises. DxContinuum is one of
a few vendors in this guide with a Salesforce security
certification and will be introducing a Salesforce Wave
Analytics capability later in 2016.
As one of the smaller and least capitalized vendors in
the market (it closed $4 million in a Series A round in
3Q15), the company is mindful about staying focused
on a “sweet spot.” While DxContinuum will pursue
midmarket deals opportunistically (companies with
at least 50 reps), it has typically sold to much larger
technology companies, with a complex product mix
that uses Salesforce. The security, data transformation
and deployment options are far more important for
larger companies. DxContinuum is a good fit on the
sales operations side for larger high-tech companies
(including those with a large indirect channel),
especially around upsell/cross-sell use cases.
Industries Represented: High tech
Supported Integrations: Marketo, Oracle (Eloqua and
Sales Cloud), Salesforce
Notable Customers: Adobe, Akamai, Cisco, VMware
Pricing: $30,000 per year for 25 users; additional
costs for additional users
Entytle
www.entytle.com
Use Cases: Upsell/cross-sell
Mountain View, CA-based Entytle is one of the newer
players in the space (with a product since 2014 and a
product shipped in 2015). It focuses on an area of the
market that has largely been ignored by other vendors.
Entytle sells aftermarket “entitlement automation”
solutions to industrial manufacturers. The solutions
utilize predictive models to identify low-wallet-share
customers and then recommend upsell and cross-sell
opportunities for spare parts, consumable items and
service contracts. Data from a range of applications
(including ERP, contact center, service and support)
feeds the model, either through real-time integration
or lightweight extracts. Since many machines and
other industrial products through an indirect channel
don’t currently “phone home,” Entytle’s solutions
help manufacturers infer usage and behavior largely
based on the “traces” they leave on different systems
and through network data of similar devices and
manufacturers. Data is presented for individual
solutions and bundles at both an account and
opportunity level. Entytle has raised $8 million in seed
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funding and will release a campaign planner tool and a
contract upsell capability later in 2016.
Entytle typically targets discrete manufacturers
with more than $200 million in revenue that want
to upsell spare parts or service contracts, replace
consumables, manage their field workforce, or gain
better visibility into the state of their installed base.
While other vendors offer predictive solutions for
upselling and may compete with Entytle in deals for
high-tech manufacturers, Entytle is often unopposed
when selling to industrial equipment manufacturers.
The aftermarket entitlement-specific use case comes
up only in a handful of predictive-analytics-related
client inquiries today, but with more than $1 trillion
spent annually on industrial aftermarket purchases,
Gartner expects to see increased interest in solving
this problem moving forward.
Industries Represented: High tech (hardware and
manufacturing), industrial manufacturing
Supported Integrations: Marketo, Microsoft Dynamics,
Salesforce (Pardot, Sales Cloud and Service Cloud),
SAP, ServiceMax
Notable Customers: Hayward Gordon, Johnson
Controls, Philips Healthcare, Teledyne
Pricing: Starts at $100,000 per year (plus a one-time
set up fee of $50,000) and increases based on the
amount of pipeline increased from
Entytle-based recommendations
EverString
www.everstring.com
Use Cases: TAM identification, segmentation, account
selection, demand generation, lead scoring, upsell/
cross-sell
San Mateo, California-based EverString started in
2014 and quickly secured more than 100 customers
and almost $79 million in venture capital funding. It
supports the full gamut of predictive B2B marketing
use cases (including ones not in the guide, such as
ad targeting) and some sales-related use cases, as
well. EverString claims to take a different approach
with its modeling than other vendors, preferring
to look at a wider range of signals to identify the
company’s “DNA” rather than traditional fit and intent
models. Its approach to creating data revolves around
extracting machine-learning insights, in addition to
crawling and ingesting data from both proprietary and
commercial sources. The company also has separate
algorithms for segmentation and scoring. EverString
targets marketers, sales development managers and
sales leaders and provides self-service capabilities
for individual marketers, such as expansion audience
building and segmentation on the fly. It helps sales
reps and SDRs identify accounts and contacts both
inside and outside their CRM system that are similar
to the ones they just closed. EverString was one of
the first predictive analytics vendors to promote its
solutions as a more data-driven way to do prospecting
and account selection for ABM.
EverString has used its venture capital investment to
aggressively fund its product development and go-to-
market efforts. This has allowed the company to go
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after a wide range of industries and simultaneously
serve the midmarket and enterprise segments.
EverString has a low entry price that is attractive to
smaller companies, but it can scale its offerings (and
the associated price) to meet the needs of much larger
companies. Given its broad portfolio capabilities,
EverString can be a fit for many North American
companies looking at predictive B2B marketing or
sales solutions. EverString was named a Gartner Cool
Vendor in 2016.
Industries Represented: Financial services,
healthcare, high tech, professional services
Supported Integrations: Marketo, Microsoft Dynamics,
Oracle (Eloqua), Salesforce
Notable Customers: Apttus, Comcast Business,
IBM, Salesforce
Pricing: Starts at $14,000 per year for the entry-level
EverString Audience Platform and increases with
database volume and additional use cases. Contacts
for demand generation are priced separately.
GrowthIntel
www.growthintel.com
Use Cases: TAM identification, segmentation, demand
generation, upsell/cross-sell
London-based GrowthIntel was founded in 2011
and has built its business selling top-of-the-funnel
predictive solutions to U.K.-based companies that
target small businesses. GrowthIntel’s solutions are
mostly used by chief marketing officers (CMOs)
and demand generation leaders to build segments
for outbound campaigns to net-new prospects, but
it also offers models for prioritizing inbound leads
and targeting existing customers. Unlike most other
vendors in the guide, GrowthIntel collects primary
data instead of relying on third-party data. This
encompasses more than 4 million small businesses
in the U.K. and is augmented with credit reporting
data from third parties and internal data to build the
models (it currently has only direct integrations with
Salesforce but can export to CRM lead management
tools). Although each client’s data is always kept
confidential, GrowthIntel also makes use of a network
effect across the interactions that its customers have
with small businesses, which provides an additional
level of prediction beyond fit.
With only a few other vendors targeting the U.K.
market and none of them really focusing on the
same type of use cases/industries (segmentation
and demand generation for companies targeting
small businesses), GrowthIntel has had the market
largely to itself. In client inquiries with Gartner, this
company ends up being mentioned in conjunction
with traditional data vendors rather than with other
predictive marketing vendors, but GrowthIntel is a
fit for U.K.-based companies that are targeting small
businesses. The company plans to expand to France
and Germany in the near future, which will broaden
its market opportunity, assuming it can collect the
primary data it needs, but it may also see greater
competition in its home market from vendors in France
and the U.S.
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Industries Represented: Financial services,
high tech, logistics
Supported Integrations: Salesforce
Notable Customers: BT, Pure360, PwC, Zurich Reinsurance
Pricing: Starts at £50,000 per year. Pricing increases are
based on the number of opportunities that were created.
IKO System
www.iko-system.com
Use Case: Demand generation
Paris-based IKO System has been in the predictive
demand generation market since 2012. It targets
Western European B2B companies (mainly in France,
but also in the U.K., the Netherlands and Germany)
that are looking to generate more leads from net-new
customers. Despite raising only €3 million in venture
funding, it has acquired more than 200 customers over
the last four years. IKO System scores accounts with
an approach toward understanding an account’s “DNA”
and scores the contacts it provides, as well. It will be
adding a lead scoring capability for inbound leads later
in 2016, but the company has also developed an inside
sales solution to augment its portfolio.
Most other vendors in the market have ignored France
and the rest of Western Europe (outside the U.K.),
and IKO System has capitalized on that void to sell
predictive demand generation solutions. Given the
breadth and quality of the Europe-centric data it can
utilize, IKO System is a fit for both local companies
and regional arms of U.S. companies that are looking
for better demand generation options, especially in
France. The inside sales solution may prove to be a
useful addition to the IKO System portfolio because
it is the only vendor in Europe that can currently
provide the combination of predictive scoring, net-new
contacts, prescriptive guidance around channels and
interactions to sales reps and SDRs, and automation
to make that process easier. IKO System was named a
Gartner Cool Vendor in 2015.
Industries Represented: Financial services, high tech,
insurance, professional services
Supported Integrations: Gmail, HubSpot, Marketo,
Outlook, Salesforce
Notable Customers: Infor, Talend, TIBCO
Software, Tidemark
Pricing: Starts at €12,000 per year and increases
based on volume. The inside sales solution is priced
separately.
Infer
www.infer.com
Use Cases: TAM identification, segmentation,
account selection, demand generation, lead scoring,
opportunity scoring, upsell/cross-sell
Mountain View, California-based Infer launched in
2010 and has signed up more than 140 customers,
the vast majority of them being high-growth SaaS
companies. The company has raised $35 million in
venture funding to support its efforts. Infer provides
separate fit and behavior models (with intent part of it).
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It also layers on a profile management capability (with
access to thousands of data points) for segmentation
and account selection, and the profiles can be pushed
into Salesforce and Marketo, enabling marketers to go
beyond simple smart list creation in the latter solution.
Infer has been actively promoting the value of its
solution for ABM efforts and has formed partnerships
with Terminus and AdRoll on the activation side
(integration is done via Salesforce). The company also
supports unique cases, such as scoring any external list
before marketers have to purchase them. Infer is one of
only a few predictive B2B marketing vendors to provide
models for opportunity scoring.
With a large volume of SaaS companies as customers,
Infer is a fit for midmarket and upper-midmarket SaaS
companies, especially for account selection and lead
scoring projects. However, newer entrants to the space
have aggressively gone after Infer customers when
their initial agreements are set to expire, causing the
company to focus on defending its installed base. As
Infer has expanded its solution set, the company has
the opportunity to expand within its own customer
base, but Gartner still expects to see Infer branch into
other markets in 2017 and beyond. Infer was named a
Gartner Cool Vendor in 2015.
Industries Represented: High tech (primarily SaaS)
Supported Integrations: Google Analytics, HubSpot
(fit score only), Marketo, Oracle (Eloqua), Salesforce
(Pardot and Sales Cloud)
Notable Customers: HubSpot, New Relic,
Tableau, Zendesk
Pricing: Starts at $30,000 per year and increases
based on the number of models. Net-new contacts (for
demand generation solution) are priced separately.
InsideSales.com
www.insidesales.com
Use Cases: Account selection, lead scoring,
opportunity scoring, forecasting
Provo, Utah-based InsideSales.com is a unique player in the predictive B2B marketing and sales application market. Long known as a pioneer in the inside sales solution market with a platform that includes a dialer, email templates and gamification capabilities, the company has also had some innovative predictive analytics capabilities with its Neuralytics engine and NeuralView product. Using network data of more than 150 million customer profiles (based on more than 100 billion interactions across its more than 3,000 customers), the company’s NeuralSort capability can help SDRs predict who to contact, when to contact them, what to say and what channel to use. It can also score accounts, leads and opportunities, providing a NeuralScore based on propensity to close. In early 2015, InsideSales.com acquired C9 (a Gartner Cool Vendor in 2015) and added its predictive forecasting capabilities (now branded as HD Forecast) to the portfolio. The combined platform provides both predictive analysis and prescriptive recommendations, with the latter being among the most extensive of any vendor. InsideSales.com has raised nearly $200 million in venture funding, including investments from
Microsoft and Salesforce.
InsideSales.com has made a shift from largely
targeting small or midsize businesses to moving
upmarket, with more than half of new sales coming
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from enterprises. It is one of the major players in
the inside sales solution market, and NeuralView is
an integral component (and differentiator) of that
platform. All the predictive capabilities are included in
the highest pricing tier, which means that NeuralView
users can add predictive account and lead scoring
for free. InsideSales.com does not provide leads or
contacts for demand generation, so it rarely competes
with other predictive B2B marketing vendors, but its
predictive marketing solutions are a fit for a range of
companies, especially those already using InsideSales.
com’s inside sales solution. The HD Forecast solution
is a fit for enterprises running Salesforce or Microsoft
that are looking to improve forecast accuracy and
pipeline visibility.
Industries Represented: Business services, financial
services, high tech, insurance, professional services
Supported Integrations: Microsoft Dynamics, Salesforce
Notable Customers: ADP, GE, Google,
Thomson Reuters
Pricing: NeuralView is part of the Accelerate edition
subscription, which is $3,540 per user per year. HD
Forecast Professional Edition costs $840 per user
per year for opportunity scoring, while HD Forecast
Enterprise Edition costs $1,080 per user per year and
includes predictive forecasting.
Lattice Engines
www.lattice-engines.com
Use Cases: Segmentation, account selection, demand
generation, lead scoring, upsell/cross-sell
San Mateo, California-based Lattice Engines began
offering predictive solutions in 2006 productized its
initial offering (an upsell/cross-sell solution targeted at
sales) in 2011. Lattice has more than 150 customers
across both marketing and sales use cases (many of
them are larger companies), has raised $75 million
in venture capital, and by Gartner’s estimation, is the
largest (by revenue) pure-play vendor in this guide.
While Lattice’s more deliberate approach won deals
with larger and more security-minded companies (both
in high-tech and other verticals), it was not suitable
for smaller companies. But in 2015 and early 2016,
the company rearchitected its solutions to allow easier
data integration and real-time performance; created
a new user interface (with all models visible from a
single portal); beefed up integrations with Salesforce,
Oracle (Eloqua) and Marketo (to aid with ABM and
other campaigns); expanded its data platform to
include international, contact and intent data; and
added self-service modeling capabilities. It also added
account selection and demand generation solutions
(which includes data diagnostics, such as cleansing
and enrichment tools).
Despite the rapid growth of several other vendors, and
an internal focus in 2015 on rearchitecting its solutions,
Lattice remains the most visible “face” of the market.
With its focus on security, level of integrations and ETL
tools, the company is a fit for enterprise clients (both
in high-tech and other industries) and/or companies
planning to deploy in multiple regions. Gartner clients
report that the company’s go-to-market approach is
unique in the way it addresses complex problems and
help customers operationalize the insights from the
models. Lattice is one of the few vendors that can
26
recommend key plays at both the lead and account
level across the entire funnel. Lattice was named a
Gartner Cool Vendor in 2013.
Industries Represented: Business services,
distribution, financial services, high tech, industrial
manufacturing
Supported Integrations: Marketo, Microsoft Dynamics,
Oracle (Eloqua), Salesforce (Pardot and Sales Cloud)
Notable Customers: Amazon, Dell, PayPal,
SunTrust Bank
Pricing: Starts at $50,000 per year and includes
unlimited models. The price increases based on the
number of contacts and sales users.
Leadspace
www.leadspace.com
Use Cases: Segmentation, account selection, demand
generation, lead scoring
San Francisco-based Leadspace was started in 2010
to provide data for demand generation professionals.
It built up a strong customer base around the quality
of its data. In 2013, it added statistical and machine-
learning models to help clients identify the accounts
and contacts most likely to buy, both in terms of
net-new companies and existing leads. Today, the vast
majority of Leadspace’s more than 120 customers
are using predictive models as opposed to simply
accessing data. However, data remains at the heart
of the Leadspace offering, and the company uses its
Virtual Data Management Platform as a differentiator.
The platform can easily bring in almost any kind of
structured and unstructured data to help clients better
understand the accounts and individual contacts.
Leadspace’s approach is to take data, enrich it and
then blend it with semantic knowledge to get around
data accuracy issues that marketers often encounter.
The company believes it not only makes campaigns
and outreach more effective, but also allows for better
segmentation at the persona level.
For most of its existence, because of its reputation
for comprehensive and accurate data, Leadspace has
competed against traditional data providers instead
of other predictive analytics vendors. (It would often
expand its footprint once it sold data.) But over the last
year and after the company’s last venture capital round
(bringing it to a total of $35 million raised), Leadspace
has become more aggressive in its go-to-market
approaches. The company tends to be a stronger fit
for high-tech and professional services companies that
have large house databases but lack confidence in the
accuracy and the quality of their data. Leadspace was
named a Gartner Cool Vendor in 2016.
Industries Represented: High tech, professional services
Supported Integrations: HubSpot, Marketo, Oracle
(BlueKai and Eloqua), Salesforce (Pardot and
Sales Cloud)
Notable Customers: Autodesk, Microsoft,
Oracle, RingCentral
Pricing: Based on a combination of platform
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functionality and data volume. Starts at $26,000 per
year, which includes enrichment and predictive scoring
for up to 50,000 records. Net-new predictive discovery
of accounts and contacts is available at additional cost.
MarianaIQ
www.marianaiq.com
Use Cases: Account selection, demand generation
Palo Alto, California-based MarianaIQ is the newest
vendor in this guide. It was founded in 2014 but didn’t
release a product or get a seed round ($2 million) until
2016. The company also focuses on a unique segment
on the market. Its primary capability is to help
marketers reach target personas at named accounts
through multiple channels, starting with Twitter and
Facebook. While some of its customers only use
Mariana’s machine-learning capabilities to match
social media profiles, others use the applications for
account selection (based on fit and intent) for ABM
programs. If you know the names of the people you
want to target, MarianaIQ can identify Twitter handles
and Facebook profiles based on first name, last name
and email collected from a Salesforce database, but
it can also match using fuzzy logic. Clients can also
simply go with the profiles MarianaIQ recommends
based on persona, segment or industry.
Compared with most other vendors in this guide,
MarianaIQ covers a very narrow niche. But with the
demand for ABM programs and the importance of
Facebook and Twitter as advertising channels, there is
potential for high demand for what it offers. Gartner
has seen the most interest for this type of solution
from high-tech companies running ABM programs that
have a more complex sales process and many buyers
to target. MarianaIQ has a deep integration (both
from an orchestration and reporting perspective) with
Marketo and integrates with Salesforce (both Pardot
and Sales Cloud) and HubSpot. It doesn’t currently
integrate with Oracle Eloqua, making it a better fit
for Salesforce customers running other CRM lead
management systems. The company has developed
some additional capabilities to help with lead
nurturing and predict the right call to action, which
may increase its appeal.
Industries Represented: High tech (primarily SaaS)
Supported Integrations: HubSpot, Marketo, Salesforce
(Pardot and Sales Cloud), Twitter
Notable Customers: Finsync, MemSQL,
WhiteHat Security
Pricing: Starts at $30,000 per year (without data)
and $70,000 per year (with data). There are additional
charges for persona creation and data volumes above
10,000 contacts.
Mintigo
www.mintigo.com
Use Cases: TAM identification, segmentation, account
selection, demand generation, lead scoring,
upsell/cross-sell
Founded in 2009, San Mateo, California-based
Mintigo started out providing data for B2B demand
generation professionals and added predictive models
in 2012 based on propensity to buy. By 2013, it had
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expanded its solutions to offer predictive lead scoring,
opportunity scoring, account selection and upsell/
cross-sell solutions. Mintigo’s initial focus has allowed
it to be much less reliant on external data providers
than most other vendors, and Mintigo does its own
intent validation, as well. The company can create
custom attributes for clients, something it believes
provides a competitive advantage. It includes real-time
data enrichment to all lead, contact or account records
in both CRM lead management and SFA systems,
where the records are scored against a predictive
model. It allays security concerns by not storing
contacts in its system. For upsell/cross-sell models,
Mintigo can score multiproduct solutions and bundles.
Mintigo recently added prescriptive sales coaching
capabilities at the lead and account levels and is in
beta with a predictive campaign tool that automatically
personalizes the message and content by persona.
Mintigo focuses on the upper end of the midmarket and
on enterprise accounts in a range of B2B industries. It
has raised $34 million in venture capital funding and
was one of the fastest-growing vendors in the market
in 2015. Mintigo has fewer customers than some other
predictive B2B marketing vendors that it competes with,
but it has been very successful in getting a foothold
within large enterprises and expanding solutions down
the funnel and to support upsell/cross-sell use cases
for large customers. Mintigo’s integrations, security
capabilities and tools make it a good fit for companies
considering more sophisticated ABM programs or when
they have more ambitious or comprehensive strategies
for predictive analytics.
Industries Represented: Financial services,
high tech, media
Supported Integrations: Adobe Campaign, Integrate,
Madison Logic, Marketo, Microsoft Dynamics, Oracle
(Eloqua and Sales Cloud), Salesforce
Notable Customers: Getty, Oracle, Red Hat, SolarWinds
Pricing: Starts at $60,000 per year, with an additional
charge for contacts for demand generation. The sales
coaching solution is priced separately.
MRP
www.mrpfd.com
Use Cases: TAM identification, segmentation, account
selection, demand generation, lead scoring
MRP is a wholly owned subsidiary of Northern
Ireland-based First Derivatives. MRP had partnered
with Framingham, Massachusetts-based predictive
analytics software provider Prelytix to power its
Delta Marketing Cloud managed services for ABM.
MRP acquired Prelytix in early 2015 and renamed
the solution Delta Prelytix. MRP has more than 350
customers across three continents leveraging its
predictive analytics software, with many (but not all)
using it to support the use cases that are included
in this guide. MRP’s models are run off the powerful
Kx database (another First Derivatives portfolio
company). The models are largely intent-based rather
than fit-based. The intent signals help marketers and
SDRs not only understand propensity to buy, but also
better understand where prospects are in their buying
journey to tailor the content and outreach accordingly.
MRP has one of the largest customer bases and a
greater geographic coverage than other providers
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included in this guide. But because MRP also provides
full-funnel managed services, many buyers don’t see
it as a direct competitor to most other predictive B2B
marketing vendors. MRP is a fit for companies that
are looking to power ABM efforts into markets that are
well-established or more transactional in nature and
where intent is the primary criteria for sellers.
Industries Represented: Financial services, high tech
Supported Integrations: Marketo, Microsoft Dynamics,
Oracle (Eloqua), Salesforce (Pardot and Sales Cloud)
Notable Customers: Cisco, CSC, Hewlett Packard
Enterprise, NetSuite
Pricing: Starts at $60,000 per year and scales
based on the countries covered, number of solution
topics and the level of customization to the scoring
algorithm, which can be modified and adapted to
specific client needs.
Radius
www.radius.com
Use Cases: TAM identification, segmentation, account
selection, demand generation, lead scoring, upsell/
cross-sell
San Francisco-based Radius was founded in 2009
and launched its first predictive B2B marketing
application in 2014. It has raised more than $128
million in venture funding, more than all but one
vendor in this guide. Initially, the company was heavily
focused around helping companies across a range
of industries better identify and target the small
businesses that had the highest propensity to quickly
purchase its solution. But over the last year, Radius
has added capabilities to address a wider range of
use cases (especially down-funnel) and to address the
needs of companies selling to enterprises. The Radius
Business Graph of 20 million businesses includes
account and contact data gathered from internal and
external sources and is enhanced by the network
data across interactions of all its customers. It more
recently expanded to include attributes that are
more important for companies targeting enterprises.
Radius has strong integrations with Salesforce and
Marketo, as well as with Facebook, which allows users
to activate custom audience campaigns to the target
account lists and segments that Radius recommends.
The company also promotes rapid self-service model
creation to allow marketers to quickly size and engage
with new segments.
Despite having more than 100 customers (with several
in the Fortune 50) and being really well-funded,
Radius has been more under the radar than some
other vendors in the guide. It has been able to sell
to a broader range of clients (half its customers are
outside high tech), and it would often go unopposed
with companies that sold to small businesses. But
the company’s visibility has increased over the last
year, especially around ABM-related use cases, such
as account selection, demand generation and upsell/
cross-sell. Radius doesn’t charge customers until
solutions are fully deployed, which removes the risk for
smaller customers. Its security model and broad suite
of offerings make it a fit for larger customers, as well.
Radius was a named a Gartner Cool Vendor in 2016.
Industries Represented: Financial services, high tech,
insurance, media, office supplies, travel
30
Supported Integrations: Facebook, Marketo, Oracle
(Eloqua), Salesforce
Notable Customers: American Express, Expedia, First
Data, Sam’s Club
Pricing: Starts at $42,000 per year and increases based
on the size of the database and additional use cases
SalesChoice
www.saleschoice.com
Use Cases: Segmentation, account selection,
opportunity scoring, forecasting, upsell/cross-sell
Toronto-based SalesChoice was founded in 2012
but released its first predictive analytics solutions in
2015. The company has an early U.S. patent filing for
predictive and prescriptive sales analytics, leveraging
diverse signals inside and outside the CRM system.
In 2016, it added two new product offerings to its
suite: Prescriptive Analytics and Intent to Purchase
(Propensity Signals). The company is small, with
fewer than 50 employees, and has taken no outside
investment to date. While it recently introduced a
propensity-to-buy module to select accounts to target
for upsell/cross-sell purposes and it can do stand-
alone opportunity scoring, its biggest focus is around
predictive forecasting for companies in the U.S. and
Canada. It uses many diverse machine-learning
methods to identify the likelihood of a win, prescribe
actions and increase the odds of winning. SalesChoice
also leverages AI and predictive constructs to
prescriptively guide sales reps around discounting
and prioritization. Each company gets its own unique
predictive model versus a subset of predictive
attributes, enriching the pattern intelligence.
More than any other company in this guide,
SalesChoice leverages partnerships with Salesforce-
based system integrators, including Accenture and
RelationEdge. It will sell direct, but it can also be part
of larger initiatives led by those partners to improve
sales effectiveness within high-tech companies.
SalesChoice isn’t as visible as some other predictive
forecasting vendors (in part because the lack of
outside investment limits its marketing budget), but it
is a fit for high-tech, professional services and media
companies running Salesforce or large enterprises
that are looking to take on bigger initiatives to improve
sales effectiveness or ABM (particularly for smaller
volumes of accounts).
Industries Represented: High tech, media,
professional services
Supported Integrations: Salesforce (Sales Cloud,
Salesforce1 Mobile App and Wave)
Notable Customers: Accenture, Digiday, RelationEdge
Pricing: Predictive and prescriptive bundle is $750
per seat per year. Intent to Purchase (upsell/cross-
sell) is $360 per user per year plus data fees. Volume
discounts are available.
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TopOPPS
www.topopps.com
Use Cases: Opportunity scoring, forecasting
St. Louis-based TopOPPS has been in the predictive
sales analytics market since 2014. It started off
selling a stand-alone opportunity scoring solution and
later added a forecasting solution. The company’s
core philosophy is to help sales leaders (executives,
operations leaders and managers) enforce better
behavior on the part of sales reps by offering more
accurate information about whether a deal is good
or not. TopOPPS also enables admins to build their
own models. Unlike most others in this guide, the
opportunity score is driven not by likelihood of close,
but whether it’s “healthy.” That health assessment is
done through SFA data but can also use email and
calendar information (through Salesforce, although
not a direct integration). TopOPPS then leverages
prescriptive analytics to suggest ways to improve
the health of that opportunity through embedded
coaching tips and alerts. Information about what
has changed (and why that has impacted the score)
is easily accessible. The company can also provide
metrics to sales leaders around whether a rep is
winning deals at the appropriate rate, selling at the
right price and holding on to deals for too long.
Despite having more than 50 customers, TopOPPS
isn’t as visible or well-known as some other predictive
forecasting and opportunity scoring vendors, While it
certainly sells its ability to improve pipeline visibility
and forecast accuracy for emerging companies,
TopOPPS’ focus around impacting sales behavior by
more accurately representing the health of a given
opportunity and suggesting ways to improve it allows
the company to target a different type of customer.
TopOPPS’ approach is a good fit for large and more
established companies that struggle to change sales
rep behavior because of the long tenure of reps and/
or a culture that is more resistant to change.
Industries Represented: Distribution, high tech
Supported Integrations: Microsoft Dynamics,
NetSuite, Salesforce
Notable Customers: Buckner Companies, Eventbrite,
Interactive Intelligence, TriZetto
Pricing: Starts at $14,000 per year (30 users) and
increases based on additional users
Zilliant
Founded in 1998, Austin, Texas-based Zilliant has
long been known for its predictive price optimization
solution, but it also offers SalesMax, a predictive
analytics application to help sales reps easily
identify what they should be selling to existing
customers. The company is focused squarely on
selling to industrial manufacturers and distributors
that have repeat purchase relationships with their
customers, differentiating Zilliant from many other
vendors providing upsell/cross-sell solutions, and
the recommendations from its models are sent to
32
sales reps via email, but are also available via mobile
devices, the web and in Salesforce. Zilliant’s roadmap
includes a number of capabilities slated for later
in 2016 to expand its account-centric approach,
with capabilities to model account-level revenue/
profit potential (based on wallet share) and to better
understand historical performance.
In addition to targeting different industries than
most vendors in this guide, Zilliant also addresses
a different set of sales processes. There are more
than 25 companies using SalesMax, and the average
number of accounts per rep is between 50 and 200.
To cover such large territories, many of those reps
have mandates to visit or call on up to 10 customers
per day. They often look at SalesMax before a call or
in the parking lot before meetings to learn what they
should propose when they get inside. Zilliant claims
that 83% of the opportunities that are presented by
SalesMax get acted on (pursued) by the reps. Reps can
also use SalesMax to identify what customers to visit
for upcoming sales trips.
Industries Represented: Distribution, industrial
manufacturing, high tech
Proven Integrations: Salesforce, SAP
Notable Customers: Dayton Superior, FleetPride, IMI
Precision Engineering, Lincoln Electric
Pricing: Starts at $2,500 per sales per year, with a
minimum of 25 reps
Market Recommendations
IT application leaders should talk with their sales and
marketing stakeholders to understand if there is an
opportunity to use these solutions to overcome more
complex buying processes or to increase the overall
effectiveness of their teams. Despite the buzz, the
market is new enough that many of your stakeholders
may not even know there are predictive analytics
solutions available that they can operate to solve their
problems. And if you work for a larger company, there
may be one of more of these solutions already being
used at a department or business unit level (especially
on the marketing side) without broader awareness at
either the IT or business level.
As you evaluate vendors, accuracy should be only
one of many considerations. While noticeably
worse accuracy would be a potential reason for
disqualification of a vendor during a bake-off, it’s
unlikely that one vendor will greatly outperform
another one. Instead, IT application leaders should:
■ Work with your stakeholders to understand the
dynamics of how your customers buy and how
your salespeople sell. Because in many cases, the
vendors can’t easily differentiate around modeling
techniques and third-party data sources, they
will often try to differentiate around go-to-market
approaches, especially who they target.
■ Look for the vendors that have customers similar to
you (and talk to their references), and pay attention
to what is really important to you from an IT
standpoint and your stakeholders from a business
standpoint. Some vendors are better-equipped to
deal with security concerns, others have modeling
33
approaches that favor a particular market type or
culture, others may have better integrations with
key systems, and yet others may provide a better
customer experience after the sale.
■ Clearly understand your own internal capabilities,
especially for more complex use cases. Even
though these predictive solutions abstract the
data science from the users, your stakeholders
may be overwhelmed if they do not receive
adequate guidance from the vendors. Unless you
have internal data science expertise or external
consultants at the ready, ensure that the vendor
you select has capabilities and programs to assist
with that needed guidance.
■ Consider whether ease of use and sales rep
acceptance are important relative to other criteria.
Some solutions have easier-to-use interfaces that
are designed for rapid adoption by sales reps,
but that may come at the cost of functionality or
signal exposure.
■ Ensure that the first-party data you provide to
vendors is high quality, especially for marketing
use cases; while the data doesn’t have to be
perfect and the vendors can cleanse and enrich
to some extent, low-quality data can significantly
impact model performance.
Gartner also recommends, before committing to
automated updates, that you ask vendors to be as
transparent as possible about how their predictive
algorithms work. The algorithms to these solutions
are far more transparent than in the past (a sign
of maturity), but you will still not have configurable
control over the calculation rules or be able to control
the results. The vendors will generally adjust the
algorithms as needed. In fact, some firms provide a
dedicated data analyst to your account — someone
who regularly reviews your results and makes
adjustments as needed. This service is useful for
mitigating the uncertainty that comes from using
a black-box service, but this does not completely
mitigate the risk involved here. Understanding how
the algorithms operate is the best guard against
unexpected outcomes.
Evidence
Gartner conducted interviews with all the vendors
listed in this guide in June and July 2016. This
research was also supported by interviews from
July 2014 through June 2016 with clients and other
enterprises that have implemented those solutions in
the U.S. and Western Europe.
Source: Gartner Research Note: G00303128, Todd Berkowitz, 7 September 2016
AI for Sales Forecasting & Sales Process Execution is published by TopOpps. Editorial content supplied by TopOpps is independent of Gartner analysis. All Gartner research is used with Gartner’s permission, and was originally published as part of Gartner’s syndicated research service available to all entitled Gartner clients. © 2017 Gartner, Inc. and/or its affiliates. All rights reserved. The use of Gartner research in this publication does not indicate Gartner’s endorsement of TopOpps’ products and/or strategies. Reproduction or distribution of this publication in any form without Gartner’s prior written permission is forbidden. The information contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in entities covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website.
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