Editor’s notE social listEning still nEEds tuning

PrEdictivE social analytics FacEs HigH HurdlEs

PrEdictivE lEad scoring MakEs tHE Winning sHot

corral the Future With Predictive analyticsThe technology has the potential to convert raw data into game-changing insights—but new challenges, like harnessing social media data, are a rodeo ride IT has yet to master.

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targeting Fresh insights

While predictive analytics is no crystal ball, businesses are increasingly turning to it to anticipate trends and consumer behavior.

The concept is not new; financial institutions have long used a form of it when reviewing loans. But the rise of machine learning and big data are unlocking new tactical and strategic possibilities—seemingly disparate data points can be connected to form clear calls to action for savvy users.

But hurdles remain. The technology is still maturing, and business processes will likely need to evolve as data-driven analysis takes a larger role in decision making.

A timely example of these issues is pre-dictive analytics for social media. Businesses are collecting unprecedented consumer data through social media sites, and there’s growing interest in the prospect of using that informa-tion strategically to get a jump on opportuni-ties or potential issues. But even the largest

companies face technological and organiza-tional challenges, as the first article in this handbook, on Wal-Mart’s analytics program, shows.

Reliable data is crucial for predictive ana-lytics efforts. But it’s hard to match the sen-timents expressed on social media to specific customers. The second article explores how the separation of consumer profiles and social media handles is proving problematic.

Predictive analytics can also help businesses increase efficiency, as outlined in the third arti-cle. Through a combination of consumer analy-sis and evaluating where a given company has had past success, the technology helps busi-nesses focus finite resources on the most promising leads. But human analysis remains a key cog in the process. n

Nathan LambSite Editor, SearchContentManagement

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social listening still needs tuning

Chandler Wilson is familiar with the chal-lenges of translating Web chatter into action-able predictive insights. He’s director of insight and analytics at Wal-Mart Stores Inc., the Bentonville, Ark., national retailer, which uses Brandwatch social media listening and analyt-ics as part of its corporate strategy team. A firm believer that analytics can provide valuable insight by drawing correlations that humans miss, Wilson would like to be able to use auto-mated predictive analytics tools that could continuously comb relevant conversations on social media platforms and then pass along recommendations as situations arise. But the technology is still catching up to his desire, and much of that process remains manual.

“From an information-to-action stand-point, I still have to look at Brandwatch and do a report … and that takes a long time,” he said. “Reporting and dissemination are time-consuming.”

Most enterprises deploy social media lis-tening to keep abreast of pertinent Web dis-cussions. Applied correctly, intelligence from social media monitoring reduces response time during a crisis and helps businesses get ahead of emerging issues.

Predictive analytics can potentially take that process further by creating a formula in which social conversation, contextual data and the gauging of current trends to predict future events can help a company capitalize on a com-ing trend or avoid an imminent problem.

But tool immaturity remains a major obsta-cle, said Real Story Group analyst Kashyap Kompella. Given the sheer volume of Web traf-fic and social conversation, predictive social analytics requires some degree of automation, which in turn requires developing algorithms that can reliably interpret sentiment and help draw correlations between key data points. Sentiment analysis tools are being piloted to

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help make those connections, but Kompella said that technology is accurate only 50% to 70% of the time.

“If you are going to make big changes based on sentiment, you would want the accuracy to be higher than it currently is,” he said. “Most of the tools demo well because they’re using very clean data sets, but when you meet messy, real-world data—which is complex, multilingual and with all sorts of new patterns that keep coming up—that breaks down.”

silos nEEd BridgEs

Kompella listed context as another key issue. Social media profiles are typically not con-nected to consumer profiles that reside in cus-tomer relationship management systems, and that makes it difficult to gauge key metrics, such as a consumer’s intent to buy.

“If a 14-year-old says he’s going to buy a Fer-rari, there’s purchase intent, but it’s probably 30 years down the road, versus an executive saying they’re going to buy a Ferrari when they get a bonus,” he said. “They both voiced intent, but from a marketing or actionable perspective,

you really need to know something beyond what is being said in the conversation itself.”

“The current challenge is that there’s the social silo and there’s the enterprise silo,” he continued. “The real ROI is when you’re able to merge these things; it’s a problem [that] has not effectively been solved yet.”

It’s not just an external issue. If software vendors can get predictive algorithms up to speed and break down data silos, Gartner ana-lyst Jenny Sussin said companies need to face the internal challenge of mapping out organiza-tional responses to the data they receive.

“A lot of it is process stuff when it comes to actionable insights,” she said. “Even if you have a predictive algorithm, if you don’t have

maNagemeNT

“ if a 14-year-old says he’s going to buy a Ferrari … it’s probably 30 years down the road, versus an executive saying they’re going to buy a Ferrari.”

—kasHyaP koMPElla, analyst, real story group

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anybody who can actually take action on the insight, it doesn’t really mean anything.”

Wilson expected Wal-Mart will continue to invest in predictive analytics tools, saying they plan on applying data-driven insights to every department, from supply chain to prod-uct sales, in the near future. But he anticipated that would likely require updating the compa-ny’s decision architecture.

“We have started to move in the direction of being more aligned strategically,” he said. “We have to monitor our internal information flow as much as the external, because otherwise we can’t leverage the information.”

Moving toWard PrEdictivE

Despite the obstacles, Kompella said busi-nesses are increasingly investing in social analytics with the immediate goal of showing clearer ROI for social media outlay. He cited recent results from market researcher The CMO Survey in which respondents reported that social media on average consumes 9.9% of marketing budgets—with that number pro-jected to hit 22% in the next five years.

But analytics is only a small fraction of that budget—about 1% to 2%, Kompella said. Com-panies still have a ways to go to incorporate analytics into business decisions.

“Most of the current analytics are geared toward vanity metrics, such as [number of] people engaged, rather than how many leads you actually generated,” he said. “The way you demonstrate ROI is through better use of ana-lytics, and that requires maturity of the tools themselves, as well as the capacity of the com-panies to exploit these tools better.”

UrbanBound LLC is a Chicago-based com-pany that produces software to help human resources departments manage employee relo-cation. The company uses HubSpot technology

analytics should do more than reinforce known insights. Predictive data should also challenge executives to see the business environment in new ways and make decisions based on the data.

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to handle social media listening and analytics, primarily for customer interaction and market-ing efforts, said UrbanBound vice president of marketing Erin Wasson.

Wasson said the tools are used primarily to evaluate which posts are creating the most new business. Content marketing is UrbanBound’s primary source of leads with social generat-ing roughly 15%—but she expected that to rise soon when the company hires a social media specialist.

“I definitely see social media growing, and we’re going to invest in the opportunities

there,” Wasson said.In general, Kompella suggested it’s a good

idea to focus on the added value when con-sidering analytics. Analytics should flesh out a picture and do more than reinforce known insights. Predictive data should also challenge executives to see the business environment in new ways and make different, even unorthodox, decisions based on the data.

“Are these analytics really helping you vali-date what you already know?” Kompella asked. “Or are they telling you something you hadn’t thought of?” —Nathan Lamb

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Predictive social analytics Faces High Hurdles

Social media listening platforms allow organizations to follow Web discussions, and there’s growing interest in the idea that analyt-ics could convert knowledge into actionable insights about the future.

Social media listening looks at what peo-ple are talking about on sites like Facebook, Twitter and LinkedIn as well as in blog posts, reviews and comment sections on the Inter-net. It’s a vast data set, but analytics offers the prospect of distilling relevant conversations into discernable signals that will help busi-nesses proactively meet consumer needs.

Using analytics to anticipate the future isn’t new, according to Olivia Parr-Rud of the Olivia Group, a consultancy specializing in predic-tive analytics. Over the past 20 years, banks, credit card and insurance companies have used that type of information to evaluate customers’ credit risk.

The difference with predictive analytics for

social media is the immediacy of the informa-tion. “It really shows what the person is inter-ested in right now,” Parr-Rud said. Businesses that have that information can make real-time offers, knowing that the offer is relevant to a customer’s current interest and therefore more likely to be acted on.

Still, using insights from today to predict the future is easier said than done. Companies often struggle with whether the insights they have gathered are truly predictors of the future. They also struggle with how to translate those insights into an action plan. And, of course, many companies have siloed departments and data that make aggregating data and creating a unified strategy an obstacle.

not HElPFul EnougH—yEt

Despite the existence of social listening tools, most companies struggle to anticipate what

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their most valued customers will do next, said Allison Smith, an analyst at Forrester Research. Smith said it could be years before that changes. In the meantime, customer insight professionals are under pressure to show ROI for the high cost of data mining.

The problem is finding the right sources of data and then tying that information correctly to a customer, Smith said. Social listening plat-forms scour the media, searching for mentions, keywords and sentiments. Of the three types of media—owned (a business website or com-pany-owned Facebook page), paid (social ads or banners) and earned (reader reviews, customer blogs)—earned media is hardest to find and track.

Once that information is found, businesses can exploit it when they know who said it and why. For example, it would be useful to know if

the person who posted negatively about your product was a longtime customer or not. Connecting that sentiment to a specific cus-tomer through a company’s customer relation-ship management (CRM) system would add context to the comment, but Smith said many CRMs are not integrated with social listening platforms.

“The piece that is missing is the data layer with the customer. [Is] this Allison Smith on Twitter the same Allison who is our cus-tomer?” Smith explained. Businesses often ask customers for their email addresses, but they perhaps should begin asking for Twitter han-dles and other social media identification.

“A lot of customers may struggle with [giv-ing up some privacy], but the brand can then provide more targeted offers and more relevant advertising,” Smith said. If customers under-stand that they get something in return, they may be more likely to divulge information.

BusinEssEs Making HEadWay

Organizations continue to look for ways to identify their target markets and send directed

Businesses often ask customers for their email addresses, but they perhaps should begin asking for their twitter han dles.

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messages to them, eventually hoping to drill down to specific customers.

Pulsar, a social intelligence platform, uses aggregated data from Facebook to help its cli-ents understand what people from certain demographics are talking about. The Food

Standards Agency, a government agency in the U.K., used Pulsar when it wanted to get the word out about the dangers of DNP, a fat-burning supplement popular among teenag-ers. Through social media listening, the agency identified where those key terms were used on social media, which indicated where the user activity was. The Food Standards Agency then targeted those areas with educational mes-sages and used influencers to spread the word in social media hot spots.

“Through the profiling of target audiences, we were able to get messages out to the right demographics, via their trusted influenc-ers, and across the channels that they use,” explained James Baker, social media manager for the agency.

As businesses look to use predictive social media analytics, Smith and Parr-Rud offered the following recommendations:

■n Know what you’re looking for, and ask the right questions to get the answers you want. In a Forrester brief on predictive social ana-lytics, Smith said that when an online ticket events company searched the name of the artist Beyoncé, that name alone didn’t cor-relate with buying concert tickets. Searching for more specific sentiments, such as favor-able comments about Beyoncé’s concerts, provides more accurate information.

■n Know how you’ll use the data to relate to the customer. Do you have a plan to use it as part of your marketing or business goals? How will you use the information to be forward-thinking and not just backward-looking?

Experts advise companies that use predictive social media analytics to know what they’re looking for, how they’ll use the data to relate to customers and how to measure their efforts.

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■n Determine from the beginning how to mea-sure efforts. Many companies consider this a midstream concern, but they should plan this from the start and choose key performance indicators that outline the results they hope to gain. But keep in mind that some results are not direct. For example, increased website activity by a customer does not always trans-late to an immediate purchase.

■n Know if you can integrate data from social listening or intelligence with your CRM data, which would give you a customized view of your target customers.

Most organizations are still maturing in their use of predictive social analytics, Smith said. These companies may understand how to mon-itor and listen to data on social media. More advanced businesses are the ones using the analysis for business intelligence. The most mature businesses integrate social media pre-dictive analytics with other sources of infor-mation, such as CRM, to provide social and nonsocial feedback.

Although it may take some time to get to that last level of maturity, the companies that do will have a distinct advantage. —Pamela DeLoatch

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Predictive lead scoring Makes the Winning shot

For many companies, today’s marketing and sales funnel is a confusing mess. The top of the funnel is swelling with increasingly more leads that are responding to an avalanche of content marketing and “freemium” or try-to-buy offers. But the bottom of the funnel can seem like it’s moving at a trickle as salespeople struggle to identify the most promising leads, fueling lower sales rates, lower average selling prices and longer cycles to close deals.

The funnel encompasses both marketing and sales processes, from a company’s initial con-tact with a prospective customer to the final sale. Marketers are responsible for the top, working to gather contact information and basic qualification data for new sales prospects. As it narrows toward the bottom, the fun-nel represents sales prospects that are further along in the buyer’s journey. The funnel can become a flashpoint between sales and market-ers when marketers provide poor-quality leads

and expect sales to close deals nonetheless. When sales fails to follow up on good pros-pects in a timely manner, it leaves the market-er’s work fallow and wasted.

For many companies, marketing automation platforms’ rules-based lead scoring does not go far enough to fix the problems with the fun-nel. Last year, SiriusDecisions found that 68% of companies used marketing platforms to do lead scoring—but only 40% of salespeople believed that it was effective. That’s why com-panies are turning to predictive lead scoring, which harnesses the power of big data to iden-tify the leads that are most likely to convert, close and generate the maximum revenue for the company.

tHE Hard nuMBErs

Predictive lead scoring can provide a “credible, third-party source of truth between sales and

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marketing,” said Melissa Davies, head of global marketing operations at SLI Systems, which uses Lattice Engines, a predictive lead-scoring technology. When salespeople focus on the top leads, sales can grow. Software vendor Infer has helped ZipRecruiter salespeople to spend 90% of their time getting high-quality leads com-pared with just 72% in the past.

By focusing on the right leads, DocuSign, which also uses Lattice Engines, experienced a 38% increase in the predictability of con-versions, according to Ryan Schwartz, former director of marketing systems and operations at DocuSign. Lattice Engines’ chief marketing officer, Brian Kardon, said that predictive lead scoring can shorten sales cycles from 180 days to as few as 100 days and can increase the aver-age deal size by 20%. Finally, continuous suc-cess metrics and automated models make it easier for companies to refine and update lead scoring to increase accuracy over time. So how does predictive lead scoring work?

start WitH a ModEl

The first step is to develop a predictive model based on a company’s particular needs. The good news is that you don’t need to hire an in-house data science team to create the model. In a few weeks, cloud-based predictive lead-scoring vendors can provide a learning database to create an automated lead-scoring model. The database incorporates the customer’s lead data, won data and other data from enter-prise resource planning, marketing, CRM and customer service systems as well as external information.

The vendor’s software then creates an auto-mated model that identifies the best positive and negative success predictors from thou-sands of potential internal and external sig-nals and attributes. Some customers will hold back a portion of their data for back testing in order to assess the model’s accuracy. Typi-cally, you need 100 successful outcomes to cre-ate a reliable model, but the vendor can provide

Predictive lead scoring uses big data to iden tify the leads that are most likely to convert, close and generate maximum revenue for a company.

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guidance based on its experience and your needs. Predictive lead scoring probably won’t help you if you’re a new company with few successful outcomes or wins.

The vendor’s analytical engine then uses machine learning to identify the top success predictors and creates a predictive scoring algorithm based on the relative weight of each attribute. The result is a straightforward lead score that might range from one to 10, indicat-ing a low-to-high likelihood to buy, lifetime value, stage of the buyer’s journey or a combi-nation of all three. Some vendors also provide a rationale for each score.

As new leads and new data come in, predic-tive models send lead scores to your market-ing and CRM systems using prebuilt APIs. “The ideal lead-scoring system should show the why

versus a black-box approach,” said Alison Murdock, vice president of marketing at 6sense. “The why tells sales reps what buyer activity led to the score so the rep can use the intelligence to drive a better-informed sales process.”

ligHting uP tHE Black Box

The software can lack transparency about some of its assumptions in lead scoring and other analysis. These technologies use algorithms that can be difficult to work with. Many com-panies want greater transparency—or control of—how these algorithms are set and what the criteria and priorities are.

The black-box nature of these models cre- ates another challenge: They may not be intui-tive to, or believed by, stakeholders. The whole idea of predictive lead scoring is to identify the factors—some expected, some not—that drive positive results. But just like any insight, predictive lead scoring requires humans to correctly interpret the data (that is, to make sure that the model is accurate). And that means bringing in not only marketers but also

the whole idea of predictive lead scoring is to identify the factors that drive positive results. But it also requires humans to correctly interpret the data.

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sales leaders and potentially even indepen-dent data scientists who can assess the model’s validity.

alWays iMProving

Expect to develop the model iteratively to improve accuracy—both before and after go-live. Using an iterative approach to model development, DocuSign’s Schwartz empha-sized, puts the focus on the accuracy of the score. Although the lead scores should be reli-able, they may also be counterintuitive to sales representatives. So you may want to keep the lower-scoring leads in marketing for nurturing, passing only the high-scoring leads to sales so they can focus on closing deals with the right

leads. “Developing accurate scoring is impor-tant, but what’s necessary for achieving suc-cess is the application of the score,” said Vik Singh, CEO of Infer. “A score itself is neither actionable nor sticky: It’s what you do with it that counts.”

Predictive lead-scoring systems also include reporting that enables marketing and sales leaders to track the effectiveness of the mod-els, so they can be modified as needed. As you add more data, the model can become smarter and may be updated on different time frames, depending on your needs.

Knowledge helps salespeople prioritize bet-ter, sell and close business. Predictive lead scoring can deliver the intelligence they need to do just that. —Steve Robins

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auTHors

PaMEla deloatcH covers business-to-business and technology issues. She has written articles, profiles and case studies for numerous organizations. Email her at pamela@b2bstorytelling.com.

natHan laMB is the site editor for SearchContent Management. He covers enterprise collaboration, content management and information governance. He previously worked as a newspaper reporter and editor, most recently with Gatehouse Media New England. Email him at nlamb@techtarget.com.

stEvE roBins is a consultant at Solution Marketing Strategies, a consultancy that advises companies on marketing and demand generation strategies, segmen-tation and messaging. Robins has held senior market-ing roles at FirstBest Systems, EMC, Documentum and KANA Software. Email him at steven@solutionmkt.com.

Corral the Future With Predictive Analytics is a SearchContentManagement.com e-publication.

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