5 Essential Signals Across the Customer Lifecycle That Every Company Needs (Part 1)

By on October 29

Predictive customer analytics are becoming a must-have for every enterprise sales and marketing organization. Using “big data” to target the right prospects, with the right message, at the right time and place is critical for success.

But too often companies settle for a convenient “point solution,” such as lead scoring, that fails to extend predictive customer analytics across the customer lifecycle.  Lead scoring is a powerful tool for culling thousands of incoming leads to a more focused set of opportunities that sales and marketing teams can focus on.  But the data needed to do powerful lead scoring – customer demographics, social media profiles, on-line content consumption, purchase history – is the same useful data for other stages during the buyer’s journey.

Here are the five core customer lifecycle analytics that every company needs to build into their predictive roadmap:

  1. Buying intent signals: Increasingly, companies can build intent monitoring algorithms to identify likely high-value prospects before they respond to your email, visit your website, or contact a sales rep.
  2. Lead scoring: As soon as prospects respond to a marketing campaign, lead scoring can use buyer “fit” and level of “engagement” to prioritize action.
  3. Content personalization: It’s not enough to know just who to target – we can also understand what content to target them with. Algorithms that predict “best messaging and relevant content” have shown to accelerate sales cycles.
  4. Best offer: By understanding the buying patterns of “look alike” prospects, or even understanding the purchase history of and existing customer, predicting the most likely to purchase product/ price bundle can increase conversion rates and transaction sizes.
  5. Customer retention and cross-sell: The amount of predictive data available on existing customers is far more than most companies realize. Building algorithms to time retention/renewal offers, predict high vs. low churn risk, and identify upsell cross-sell opportunities can have a material revenue and profit impact.

Few companies can implement all these predictive tools at once, and in fact, each one by itself can improve pipeline volume, velocity and conversion rates. But more importantly, all five draw upon a common set of customer data – and developing a full set of customer lifecycle predictive analytics can take sales and marketing organizations leaps and bounds beyond the exclusive focus on lead scoring that every competitor is implementing.

Follow our series for a deep dive on each of these topics. Read Part 2!

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