Mark is your next customer. How do we know?
Buyers leave countless and very traceable footprints as they research for products online. Marketers call this the ‘digital body language’ of a user, as it is composed of a myriad of complex behavioural traits like –‘appeal’ or ‘attraction’ that causes a pull towards a particular product among similar others, ‘signs’ of interest in the merchandise, the ‘what’ and ‘how’ of the problem a particular product solves, and the often intricate reasons for the ‘choice’ or ‘loyalty’ towards a specific brand. Predictive analytics collates these innumerable subtle or explicit behaviours using customer data from across the organization and ‘profiles’ the ‘right’ customers, and then proceeds to prescribe a powerful roadmap to engage them. The technology savvy buyers in this age and time are knowledgeable, informed and alert. Most of them search, research and read online to educate themselves regarding products and services understanding that it is the fastest and easiest way to get appropriate information, compare pricing, read reviews and get advice.
Shopping experiences are subliminally driven and subconsciously filtered. It is therefore vital for marketers to steer away from the traditional way of thinking and of aligning their buyers to the outdated and outmoded marketing ‘purchase funnels’. It helps instead to think about it as a cycle, managing the end-to-end customer experience within this cycle and accelerating the customer journey towards the purchase of a product. Predictive analytics can empower marketers. The marketing focus is optimized and campaigns are more successful when the ‘signals’ that the decision makers are throwing at us are carefully screened, read and used. The most qualified leads with the maximum potential are generated from among those in the user base, by tapping into behavioural data from CRM, marketing automation platforms and the activity in the greater internet/web area to get an all-around picture of the customers and to identify buyers who are actually in the market to buy relevant solutions. Inbuilt heuristics and statistical models are used to generate metrics for scoring and ranking these leads, giving you an accurate picture of whom to target specifically. The more that is known about buyers the more the communication and message can be appropriately tailored to meet and alleviate user pain points, leading to better conversion rates from marketing-qualified leads (MQLs) to sales-qualified leads (SQLs).
Campaigns are wisely crafted and click-through rates are dramatically improved. Using predictive analytics, customer segmentation can be made more sophisticated. There is an endless scope to customize and personalize content, which allows for users to be clubbed into a ‘segment of one’ and coaxed to interact with the brand in an extremely individualized manner. With a powerful ‘triangulation’ technique that enables marketers to link behavioural data sets with psychology around multiple factors such as the weather forecast, a local football match or an anniversary or birthday, certain attributes are combined, correlated and tweaked to turn a prospect into an ever loyal customer. Useful information such as – what marketing efforts were most effective in eliciting the desired response from a specific customer – can be strategic in engaging the purchaser, can lead to a mutually meaningful relationship for both the buyer as well as the seller, and can last
throughout the customer life cycle. An added advantage of predictive analytics is in its use as an early warning system notifying you of trends that signal customer attrition. With all the customer profile information at your disposal, a significant use case can be constructed for retaining the customer and rekindling the spark that driveshis attention back to your product. This way the unwavering eye of the predictive analytics tool keeps track of your customers, especially those with a high attrition risk who would have otherwise been easy to lose, and helps in continually bringing them back into the fold. Predictive analytics is packed with fantastic features like a ‘next best action’ prompt that indicates the best offer or communication to send to a given prospect or customer in case upsell or cross-sell opportunities exist or where such opportunities can be created. In this way, predictive analytics leverages algorithmic models and lookalike modelling not only to predict each prospect’s propensity to buy but to provide an improved customer experience, that is customer focused and customer driven.
How is Mark the next customer, then? Predictive analytics answers taking this example where Mark is recognized and ‘profiled’ as a sports enthusiast and an account of his past and present shopping behaviours is available. The fact that Mark had purchased a jersey for his favourite football team last year is taken as an indication that when the same team plays in the finals this year, Mark may be in the market again for more of the similar products, such as caps, collectables and souvenirs. Given all the statistics supporting this prediction, we can comfortably target adverts that are tuned specifically to Mark’s tastes and preferences and await splendid outcomes.
Columbus, L. (2016, January 24). 89% of B2B Marketers Have Predictive Analytics On Their Roadmaps
For 2016. Retrieved October 13, 2016, from Forbes.com:
Fisher, L. (2016, March 17). How to make the most of predictive analytics. Retrieved October 11,
2016, from Marketing Week: https://www.marketingweek.com/2016/03/17/never-look-
Purchase Funnel. (n.d.). In Wikipedia. Retrieved October 12, 2016, from
Raab, D. M. (2013, October 2). Idio Does Sophisticated Content Recommendation. Retrieved October
14, 2016, from Customer Experience Matrix:
Wallace, M. (2015, February 3). Exploring The Cutting-Edge: Predictive Marketing Analytics.
Retrieved October 11, 2016, from Marketing Land: http://marketingland.com/another-prediction-this-one-is-85-accurate-114919