leveraging digital transformation to find new revenue & more · by moving to a data-driven,...

AN EXL WHITE PAPER

Leveraging Digital Transformation to Find New Revenue & More Profitably

Nagaraja SrivatsanChief Growth Officer, EXL

Vivek JetleySVP & Business Head of Decision Analytics, EXL

Dheeraj PandeySenior Manager, EXL

Sanjay ReddyVice President & Head of Client Management, Transportation and Logistics, [email protected]

Written by:

But essentially, every sales puzzle boils down

to identifying the right customers, targeting the

most profitable territories, identifying the most

and least effective sales agents, connecting

with the right customers and channels,

understanding the best ways to close sales

and figuring out what factors can influence

performance across the sales cycle.

The prospect targeting process in most

organizations follows a very distinct pattern.

A simple approach to untangling the sales

puzzle is to divide it into five distinct stages:

identifying prospects, identifying the right

timing for reaching out, finding right mode of

communication, creating saleable solutions,

and closing the deal.

Success in the first stage for B2B market

depends on identifying right contacts in the

target organization, and in B2C it is about

identifying the right market segments

aimed at getting serious consideration from

the prospects. For B2B markets this stage

involves knowing who makes the decisions

and who has the influence, and who to

contact to secure the right meetings.

Success in the second stage, timing the

contact, depends on the salesperson

acquiring precise and timely information

about relevant opportunities (ideally, ones

that competitors don’t know about), and can

be through referrals, introductions, or past

contacts outside the seller’s organization in

the marketplace at large.

The third stage, figuring out the right touch

points, should involve devising a multi

touch point strategy based on customer

personas. Success here depends on

categorizing the prospects in terms of

customer personas based on past behavior

and other available data.

The fourth stage involves understanding the

potential customer’s underlying problem.

“How can we grow our sales, increase revenues, and do it more profitably than we’re doing it today?”

That’s the elusive holy grail of business — finding a way to grow revenue in a more profitable way.

Although it seems like a simple proposition, generating more sales, more profitably, in less time has

been difficult for most companies to achieve.


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The salesperson needs to have an idea

about the needs of the organization to help

showcase the relevant products or services.

Success depends on the seller’s ability to

identify the components of the solution, and

on mobilizing and coordinating resources to

offer the right solution.

In the final stage, closing of the deal, the

objective is to remove uncertainties in the

mind of the customer and build the trust

and confidence required for the customer

to buy the product or service. This can

often be in terms of providing assurance

of after-sale support and round-the-clock

availability to address any concerns.

How Digital Technology and Advanced Analytics Disrupt the Sales ProcessDigitally transforming the sales process

involves using data, analytics and digital

technologies to enhance customer

relationships, reduce cost and improve

customer engagement.

By applying the right combination of digital

interventions, supported by analytics and

complemented by human engagement

throughout the sales process, companies

can drive sales more profitably. This process

starts with engaging customers in the most

cost-effective manner through targeted

messaging, and a digitally-enabled, data-

driven approach to conversions that

reduces the overall cost per sale.

The framework shown below outlines

the decision support factors that help

companies make sales recommendations

and feedback mechanisms to further refine

the recommendations. Each step can be

enhanced with the help of data enrichment,

advanced analytics and digital strategy.

Data

Analytics Digital

Who to Contact

When to Contact?

How to Contact? What to Sell? How to Close


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Figure1: Analytics-driven sales effectiveness paradigm

The following are the questions this sales

framework is meant to answer:

• Who to contact? Generate more high-

quality leads

• When to contact? Finding best time to

contact to increase the percentage of

conversions

• How to contact? Increase the percentage

of conversion by leveraging the right

touch points

• What to sell? Increase the percentage

of conversion by offering customized

product and services

• How to close? Cose deals more quickly

Feed

bac

k

Who to Contact?

When to Contact?

How to Contact? What to Sell? How to Close?Business

Decisions

Decision Support

Enablers

• Lead Generation

• Advisor Network

• Client Profiling

• Response Rate

• Life Time Value

• Best Time to Reach

• Triggers for Pushing Sales Macro/Micro

• Attrition Alerts

• Reasons to Call

• Preferred Channel of Communication

• Sequencing and Prioritization

• Recommendation Engine

• Opportunity Sizing

• Propensity to Buy

• Tactical Suggestion

• Transfer Learning

Act

ions

Recommendations

Measurement

Data Enrichment Advanced Analytics Digital Strategy

• Demographics• Opportunity/Market Share• Transactions & Assets• Sales & Marketing Actions• End-Customer Intelligence• Market Trends

• Machine Learning• Deep Learning• Graph Analytics• Robotic Automation• Natural Language• Computer Vision

• Paid Media• Emails• Search• Content• Social Media


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Who to Contact? Generate More High-quality Leads

The desired outcome:

Key metric: Lead Quality

Quickly identify the quality prospects most

likely to purchase the company’s products

or services, enabling the sales team to

concentrate time and effort on the people

or businesses most likely to buy.

The current process:

Many companies take a scattershot

approach to identifying prospective

customers by using blanket marketing

techniques, email blasts or cold calls to

uncover viable prospects. Prospecting is a

numbers game, based on the premise that

after a certain amount of “no’s” someone is

finally bound to say “yes.”

It’s easy to understand from an efficiency

and results standpoint why this isn’t a good

customer acquisition model. How much

time, effort and manpower are companies

spending on all of those “no’s”?

A digitally transformed process:

By moving to a data-driven, analytics-

empowered approach to prospecting,

companies can use sales resources more

efficiently, spend marketing dollars where

they generate the most benefit, and close

more sales, more quickly.

This applies to both B2C and B2B models.

In a B2C model, the sheer number of targets

to be identified and engaged to move the

needle is so vast that most companies

resort to a scattershot marketing approach.

This not only increases the cost of sales,

but targets a wide array of consumers who

never actually need to be contacted in the

first place and ultimately perceive these

messages as spam — which does nothing

to close sales or help the company’s

reputation.

A digital acquisition model changes this

approach by applying data to reveal better

prospects to focus marketing efforts.

Today, everyone has a very long data trail.

A digitally transformed prospect-to-lead

conversion process applies analytics to

pinpoint the individuals most likely to buy,

or at least have an interest in the product.

Step one is collecting a variety of third-party

data on various topics, including:

• Demographics

• Psychographics


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• Credit risk

• Life events

• Social media interactions

• Other pertinent data

Then, by applying analytic algorithms

and predictive modeling, companies can

narrow this list down to the real prospects,

changing a number game into a targeted

marketing effort.

B2B targeting is slightly different, as it is

more focused on expanding coverage

and influencing advisors by using network

analytics. B2B prospecting is focused on

building a high volume of contacts, because

more contacts result in more leads, creating

higher chances of multiple contacts talking

to the lead of interest.

The first step involves studying the various

attributes of contacts with relationship links

to the prospect, including:

• Location history

• Employer history

• Education history

• Events and conferences

• Social networks

• Professional affiliations

The second step is network analysis, which

leverages existing contacts to identify

new leads based on advisor networks

and connectivity. This helps in spotting

key opinion leaders based on emerging

clustering patterns. This is followed by

prioritizing the most promising contacts

and getting referrals, introductions and

connections from the most influential

opinion leaders.

The result:

Instead of cold-calling hundreds of people

or businesses, an inside sales team has

a much smaller, laser-focused list of

prospects segmented by propensity, life

stage or other appropriate characteristics.

As a result, companies only expend

resources on targeted potential buyers

resulting in less wasted time, reduced cost

and improved outcomes. The sales team

makes fewer, more effective calls, leading

to more sales.

For businesses that have a long tail of

prospective customers but not enough

sales coverage to contact that list, this

approach is particularly beneficial.


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When to Contact? Finding Out Best Time for Contact


Key metric: Right party contact


Many organizations don’t prioritize calling at

the right time for their campaigns. This leads

to a high incidence of lost revenue, either

because of attrition as a result of repeated

calling or because the acquisition process

could not establish the right party contact.


To get the maximum efficiency from the

calling campaigns, the company should

ensure that customers are contacted at the

right time, preventing the loss of revenue

from the inability to establish the right party

contact.

A digitally transformed process uses

analytics systems that can identify attrition

hot spots within the customer base. Scores

Salesperson Persona

Triggering Prioritized Contact List

Method of Communication

Proportion above

Digital Dan Launch of a new product with better features

in comparison to competitors

All ‘Digital’ prospects with

recent interest in such an offering

Email and interaction via digital channels,

followed by a request for setting up a phone

call

The new product and how it can address the

customer’s needs

Social Selina Dissatisfaction with an existing product/service provided by competitor, gathered through social

profiles of the prospect

All prospects with social

profiles in the database and a social touch point trail of

communication

Reaching out through social channels,

along with a follow-up call and meeting

Strong performance of the company’s

product

Fickle Freddie Recent inquiry on a product or service

All prospects with a tendency

to switch

Quick phone call Reinforcing the switching behavior to

cross sell


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built using predictive models based on

calling frequency can be automatically

passed on to relationship managers,

enabling a multi-touch strategy to establish

the right party contact, thus preventing loss

of revenue.

Determining the best time to contact

customers in a multi-channel world and

ensuring that the right party is contacted

through their preferred channel of

communication can increase the likelihood

of response.

For example, consider the case of the cross

selling to an existing customer. A data-

centric, predictive analytics approach can

improve response rates by scheduling the

right communication interventions in its

cross selling campaign, achieving increased

revenues.

In a B2B scenario, the sales cycle is usually

longer. Achieving better responses here

requires leveraging past behavior and

current events to predict the best triggers

for communication. The challenge is always

to find the right touch point and time to

discover the missing pieces in the client’s

information that need connecting, and

adding value in the process.

Achieving this calls out for mapping

the personas of the salesperson and

the prospective clients, advisors and

influencers, and optimizing interaction using

event-based triggers.

Geo-targeting is another example of

leveraging digital transformation in the

customer acquisition process. Geo-

targeting allows a business to restrict

its reach to consumers only located in a

defined geographic area. This can help

retailers with brick-and-mortar stores

in delivering great content to someone

actively seeking the product or service at

the moment. Similarly, in a B2B context, it

can be used in providing support to sales

teams in certain states, or reaching out to

audiences when hosting regional seminars.

The result:

Companies should figure out the right time

to contact new customers and identify

customers that will attrite. This along with

customized messages, and using the right

channels can help in working towards the

overarching goal of improving revenue.


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How to Contact? Using Digital Channels to Capture New Customers


Key metric: Increasing the number of

customers acquired without human contact.

Converting more lower-complexity

prospects into sales without involving a

company’s sales team to reduce the cost of

sales and bring new customers.


In the past, sales opportunities were

driven by manpower. A sales team could

only contact a certain number of people

in a day. B2C companies relied on door-

to-door sales agents and cold-calling

for sales. This activity was supported by

inbound marketing, email prospecting, sales

networking, and referrals.

Now, digital channels have becomes an

attractive medium for customer acquisition

for both B2C and B2B companies.


By using analytic insight in conjunction

with the right digital channels, companies

can effectively close sales without human

intervention.

For example, say an insurance company

wants to expand its reach in San Antonio

without adding sales people. The

prospect analysis indicates that 150,000

people are good candidates for one

or more of its products. The data also

indicates that 100,000 people are more

likely to respond to a mailer with an online

call-to-action, while 50,000 are more

likely to respond to an email with a URL

link. Communication can be sequenced

and prioritized to these sets of customers

using analytical models on their worth,


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and best the times and days to maximize

responses and sales.

In this example, the insurer can contact

those 150,000 individuals in a matter of

days, not months, without taxing its sales

force.

When those prospects respond by

following the link, they see personalized,

contextual messaging that’s specific to

their profile, offering the kind of products

they most likely need. The digital channel

becomes an extension of the sales force,

powered by analytic insight.

In a B2B situation, the key is to leverage

content management to orchestrate

a strong targeting strategy, develop

highly personalized offerings that drive

conversions, ensure compelling device-

agnostic experiences in all interactions

and unlock the insights hidden in the vast

amount of interaction data available across

channels.

This omnichannel approach, fueled by

the use of digital and analytic tools, also

enables companies to gain revenue over

B2C channels by taking advantage of the

long tail customers that the company

previously missed.

The result:

Companies have new strategies to

cost-effectively convert low-complexity

prospects into customers. As a result,

they increase the monetary value of these

relationships without engaging inside

sales staff.


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What to Sell? Customizing the Offering and Increasing Wallet Share with Existing Customers


Key metric: Sales volume and percentage

of existing customer wallet share.


Customizing products to customer needs

results in more sales, and increasing the

wallet share of an existing customer is

often a cheaper way of boosting revenue

than increasing market share. The first step

to calculating a company’s current wallet

share is to have accurate and reliable data

on the true potential of existing customers.

Many companies do not use digital tools to

build offerings and evaluate their potential.

They typically rely on their sales staff to

capture this information in CRM tools based

on conversations with existing customers.

This is an extremely expensive way of

gathering this information, especially for

companies with a long tail of customers.

It is also fairly ineffective since it depends

on the sales reps accurately logging these

conversations, and customers showing

interest in sharing information. As a result,

the pricing and customer touch point

cadence is inaccurate, and companies

leave a lot of inexpensive revenue on the

table.


B2C companies derive great insights from

tracking how their users observe, follow

and ignore recommendations on their

websites. This can help in making pricing

and bundling decisions, personalizing offers,

identifying specific customer segments and

tracking emerging customer segments, all

the while maximizing revenues during the

process.

For existing customers, companies have

the opportunity to develop accurate wallet

share models by merging internal data

with industry trade data. These models can

be used to create accurate wallet share

segments based on actual spend data. It

is very easy to set up digital pilots on new


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products and services, which can help

in creating look-alike recommendations,

opportunity sizing, and uncovering the

propensity to buy.

In a B2B setting, leveraging advisor

personas and product holdings to identify

most relevant products becomes even

more important. The relevant product

recommendations can be:

Collaborative filtering recommendations,

using a customer’s product affinity based on

past holding patters across clients.

Content based recommendations, which

look for content similarity in creating look-

alike salespeople with similar portfolios.

Context based recommendations,

which are based on personas, clusters or

transaction patterns.

In any particular situation the final

recommendations can be based on a

mix of these techniques. This should be

followed up by opportunity sizing. For

instance, in a particular situation based on

the product recommendation approach

discussed above, three or more relevant

products could be identified. However,

only one of these products could have

a significant opportunity size and be the

priority for cross selling

The result:

Sales resources can focus on customers


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segments that offer the greatest potential to

increase revenue.

How to Close? Using Minimal Time and Few Interactions as Possible


Key metrics: Lead conversion or percentage

of lead-to-closed business.


In the current landscape, converting a

lead into a sale can be a time-consuming,

complex process. Even with a strong

prospect list, the inside sales team still

has to spend time tracking down the right

person in the right department who actually

makes the buying decision. This process

increases the amount of time spent on

getting the sale, which reduces sales output

and decreases profitability.

Lead-to-sale in B2C focuses more on

responding to an email with offers these

days. The offers are frequently not

customized based on prior customer

behavior and purchase journey data,

resulting in poor lead conversion.


Through engagement analytics, companies

can reduce wasted effort. Prior to the

first contact, they can determine the best

way to navigate procurement, finance, or

appropriate department as well as know

the name of the person or people with the

power to make the purchase.

Customer engagement analytics looks at

the pages customers have visited on the

website, the content they view about the

products and services, or the products and

services customers are not buying but may

have a propensity to buy.

The digital tactics involve sales people

using digital tools and social media to

directly address customer questions, create

and share insightful content with their

target audience, and provide value through

industry insights. The ultimate objective is

to build trust with customers, and convince

them to invest in the company’s product or

services.

This helps improve pipeline management

and sales reporting. In a typical sales

organization, numerous people handle

sales reporting, with each report containing

a broad range of data points. With digital

transformation, the sales team isn’t simply

pushing out reports, but pulling the specific


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insights management needs to see. Sales

leaders can not only quickly see if actual

sales are on target with projections, but can

access the supporting data to understand

why the actual sales may not be as

projected. Digital transformation using tools

such as an AI-driven system also enables

passing on any shared learning to others

in the team, and easily getting tactical

suggestions.

The three pieces of information used by an

AI-driven system include:

Salesperson profile: This includes

salesperson qualities including consultative,

relationship oriented, order taker, or super

closer.

Historical sales actions: This includes the

relationship between the client or prospect

and the salesperson.

External events: The include external

events which may act as a catalyst or

deterrent for a sale.

The outcome of this AI-supported sales

closing mechanism is more suggestions and

more information on the client preferences.

This includes highly customized suggestions

for each individual salesperson, triggers and

notifications for quick actions in response to

market events and the integration of human

and digital tactics.

The result:

Using engagement analytics, companies

can identify the decision-makers and

understand the supplier onboarding

process before making the call, enabling


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them to close the deal in fewer steps, with

fewer calls, and with reduced cost per sale.

The New Path to ProfitabilitySo often, conversations around digital

transformation focus on the back-office

efficiency or customer engagement.

However, by strategically using digital and

human channels powered by analytics,

companies can drive growth, decrease

cost per sale, and focus internal resources

where they bring the greatest returns.

The keys are:

• Begin with the key business decisions to

be made

• Align goals and desired outcomes

• Apply the right combination of digital tools

and analytics

• Use predictive analytics to gain a full view

of the customer and take preemptive

steps in reducing churn and cost while

maximizing revenues.

• Work with transformation providers

who can help reduce risk and augment

profitability

This is the new approach to increase top-

line revenues.


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