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Predictive Analytics Will Transform B2B Sales & Marketing Execution

11 Sep

Consumer marketers have become adept at driving revenue based on predictive analytics. Potential customers are routinely scored on a wide variety of attributes from lifestyle to promotion receptiveness.  These scores allow consumers to be  segmented into groups based on shared interests, purchase likelihood, and total buying power. By starting with highly differentiated segments, marketers can design programs that are highly relevant and effective.

This is not the way that B2B sales and marketing works in most organizations today.

Yet, B2B is a ripe environment for predictive analytics: selling costs are high, sales probability is low, and resources are very expensive. While the language of B2B marketing and sales is full of references to probability — customer funnels, response rates, conversion rates, close rates, call-to-close ratios — it’s rare to see B2B organizations leverage prospect and customer data to score customer attributes, build discrete segments, and allocate resources to maximize the conversion and revenue.

But all of this is about to change. Over the next five years, common consumer marketing techniques will find a happy home in many B2B marketing and sales organizations.

Here are 6 reasons why:

  • Electronic sales processes are creating massive amounts of useful data: Today, B2B buyers spend more time interacting with companies online than they do with sales people in person or over the phone. For every successful sales call they attend, a typical prospect may spend hours interacting with content, reading forums and blogs, and testing sample products. In today’s world, every buyer action leaves a trail of digital clues that signal their context, needs, purpose, and intent.
  • Prospect attributes can be easily deduced from observable data: Most B2B organizations with CRM and content marketing capabilities have enough data to score prospects on purchase probability, likely problems or interests, and potential solution needs.
  • Relevancy matters: Even as the typical portfolio of products and solutions becomes more varied and complex, B2B sales and marketing messages tend to be narrow and simplistic. The patterns that work most consistently are destined to be forever repeated. For prospects, this means that they are often hit with messages and a pitch that ignore the nuance of their particular needs and segmentation. For many prospects, this is a turn-off that is difficult to reverse.
  • Sales & marketing funnels are based on probability: Typically, 2% of targets respond to a marketing campaign, 60% of leads are accepted by sales, 50% of accepted leads become opportunities, and 25% of opportunities close. When you look at the full marketing and sales funnel, a pathetic 1:667 targets becomes a closed deal. Using predictive analytics to improve any stage of the funnel has the potential to create incredible value. Continue reading →

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The Frightening Science of Prediction: How Target & 10 Others Make Money Predicting Your Next Life Event

6 Sep

If you are planning to go out tonight to play billiards at a bar in Montreal called Sharx, you might want to pay with cash. Predictive analytics from Canadian Tire demonstrated that Sharx customers are the more likely to default on credit than patrons of any other drinking establishment in Canada.

The analysis by Canadian Tire, now a decade old, marked the beginning of a wave of predictive analytics that now has companies trying to guess your next move — no matter how private it may be. Are you going to get married? Are you having a baby? Are you likely to get a divorce? Might you run into financial problems? Are you changing jobs or moving cities? Who are you going to vote for? Who are you likely to buy something from? What big purchase are you likely to make next? When will you get sick? How soon might you die?

If there is value in the answer to any of these questions, there is likely a company that is actively trying to predict the answer based on the data they have on you. As credit cards and loyalty cards have become omnipresent, more and more companies are now able to mine deeply personal data patterns to predict private behavior.  According to the New York Times, “Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a ‘predictive analytics’ department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them.”

Here are 11 real examples of how companies are trying to predict your next life event:

1. Predicting Pregnancy (Target): Target uses a statistical model to score every female customer on the likelihood that they are pregnant. It can accurately predict when a shopper is pregnant early in the pregnancy and her rough due date. As reported in the New York Times, Target’s data scientist is “able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a ‘pregnancy prediction’ score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.” According to a Target data scientist who was quickly banned by the Company from talking to the press, “We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years . . . As soon as we get them buying diapers from us, they’re going to start buying everything else too.”

2. Predicting Divorce (Credit Card Companies): In the book Super Crunchers, a Yale professor describes how a major credit card provider uses purchase data to predict divorce, which in turn, helps the company predict potential future credit problems.

3.  Predicting Financial Problems (Canadian Tire): According to the Daily Beast: “Cardholders who purchased carbon-monoxide detectors, premium birdseed, and felt pads for the bottoms of their chair legs rarely missed a payment. On the other hand, those who bought cheap motor oil and visited a Montreal pool bar called ‘Sharx’ were a higher risk. ‘If you show us what you buy, we can tell you who you are, maybe even better than you know yourself,’ a former Canadian Tire exec said.”

4. Predicting Your Next Vote (Obama & Romney Campaigns): Both major parties maintain broad voter databases appended with detailed demographic information. Using psychographic profiling, they are able to predict who you will vote for, how likely you are to go to the polls, and the potential for them to change your vote. Using this data, they are able to drive targeted media strategies and send volunteers to the right doors to maximize impact on the election. As reported in the Washington Post, “If you use Spotify to listen to music, Tumblr to consume content or Buzzfeed to keep up on the latest in social media, you are almost certainly a vote for President Obama. If you buy things on eBay, play FarmVille or search the web with Bing, you tend to favor former Massachusetts governor Mitt Romney.”

5. Predicting When You Will Switch to Fedex (UPS): UPS uses data analytics to predict when customers are at risk of abandoning the company and switching to one of its competitors. Whenever a potential switcher is identified, the company tries to prevent the loss with a phone call from a salesperson.

6. Predicting How Influential You Are (The Palms): Third party companies like Klout have built complex algorithms for assessing the social media impact of an individual. If you complain online, it’s your Klout score that will often determine the response. But now, companies like The Palms and Gilt Groupe are using these social media influence predictors to differentiate between customers. According to AdAge, “The Palms’ chief marketing officer, Jason Gastwirth, is currently building out ‘The Klout Klub,’ which ‘will allow high-ranking influencers to experience Palms’ impressive set of amenities in hopes that these influencers will want to communicate their positive experience to their followers.’ The Palms is already pulling in data from Klout and referring to it as part of their reservations process.”

7. Predicting How Much Money You are Willing to Lose (Harrah’s): According to the Daily Beast, “With its ‘Total Rewards’ card, Harrah’s casinos track everything that players win and lose, in real time, and then analyze their demographic information to calculate their ‘pain point’—the maximum amount of money they’re likely to be willing to lose and still come back to the casino in the future. Players who get too close to their pain point are likely to be offered a free dinner that gets them off the casino floor.”

Continue reading →

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HBR: Big Data for B2B May be Overhyped

23 Aug

A recent Harvard Business Review blog post provides a thoughtful analysis of one of the looming issues facing B2B marketers: how to think about Big Data. As the hype builds, marketers are looking closely at their CRM data (and data in other systems) to see how to best leverage it to drive revenue.

The post asserts that broad data analysis is overrated and that marketers should focus on the narrow set of insights that really matter in a B2B sales environment. Here is the guidance on B2B Insights:

“It’s impossible to know all the insight types that you can hope for, but it is imperative that you have several to bank on that can help sales force members answer key questions. For example, salespeople can gain insight into which customer to target, which offers maximize value for each customer, or how to spend time to drive success. Sales managers can gain insight into what guidance to give salespeople, how to set goals that are fair and realistic, and how to keep a team on course to achieve district goals. Sales leaders can gain insight into how many salespeople are needed, how to attract and retain top talent, and whether an incentive plan is motivating the right kinds of sales activity.”

B2B sales is always more complicated because the sales person has a relationship and often knows more about the prospect than the data can tell. The key to providing value to sales people is to help prioritize activity: helping them figure out who to call, what to focus on, and what activity to take next to maximize their chance of success.

The full post can be read here.

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McKinsey: 2/3 of Companies Betting Heavily on Digital Business to Drive Revenue Growth

30 May

A new study by McKinsey & Company on “Minding your Digital Business” examines the rapid growth in corporate investment on Big Data & Analytics, Digital Marketing & Social Tools, and Flexible Delivery Programs and the sky-high revenue expectations that businesses are attributing to these new digital business strategies.

 The fact that McKinsey completed the study as a “Digital Business” report and not an analysis on analytics, sales, or marketing is a reflection that increasingly every business is a digital business. According to the study, 68% of companies rate “Digital Marketing & Social Tools” as a top corporate priority, 65% rate “Big Data & Analytics” a top investment priority, and 56% says the same for “Flexible Delivery Programs.”

The expected impact of these digital business strategies is sky high: 66% of companies expect a positive impact on operating income over the next 3-years. Of this group, 35% expect an increase of 10% or more.

For companies, data and analytics will likely be the biggest driver of business impact. As digital interactions with customers generate unprecedented reams of data, companies are organizing to turn insights into revenues.

Practically speaking, this is easier said than done. Companies report that organizational structure, IT infrastructure, a lack of quality data, and internal leadership gaps limit their ability to achieve their digital business objectives.

See the full study here.