Using Predictive Analytics to Engage Customers

“Combining predictive analytics with cloud technologies can enable marketing research to take new strides that can dramatically improve customer engagement and retention,” – Ryan Gould

Technology and technological progress have been traditional handmaidens of commerce since time immemorial. The Assyrians, Greeks, and Romans used appropriate seafaring technology to conduct sea-borne trade, while the modern titans of commerce use data sciences and predictive analytics to create value propositions for individual customers. We must note that data has been hailed as the new ‘oil’ and therefore, the increasing uses of data may create fundamental changes in the creation and operation of business enterprises. Meanwhile, electronic connectivity, the advent of the global Internet, the incremental growth in raw processing power, and modern data-driven technologies have enabled the generation and analysis of massive volumes of unstructured data that can be examined to boost customer engagement in modern times. Customer feedback, Internet surveys, questionnaires, and online conversations are some of the processes that generate prime data that powers the science and craft of predictive analytics.

Modern business enterprises are well-positioned to leverage analytics technologies in a bid to predict future customer behaviour and consumer choices. Predictive analytics develops these scenarios by assessing historical customer data, product usage, and consumption. Brands and businesses can utilise such technologies to identify future marketing opportunities. For instance, an e-commerce operator can choose to deploy predictive analytics to analyse customers’ buying behaviour and online browsing patterns. The outcome of such analyses may indicate that a certain set of customers remains partial to new products and recent offerings. In response to this emergent fact, said brand or business can chart a selling strategy that directs special offers on new merchandise to said customer segment via email, text messages, social media posts, etc. The operative idea in this strategy is to offer special treatment (in the form of preferential market access) to said customer segment. This instance vividly illuminates the higher level of customer engagement that can be affected by using predictive analytics.

Modern data sciences can be harnessed to help brands and businesses gain a better and deeper understanding of customers’ unique habits, requirements, and purchasing power. This information is gleaned by monitoring and following the browsing histories of individual customers through the use of software ‘cookies’. Once the information is complete, brands and businesses can utilise predictive analytics to send targeted online advertisements to appropriate customer segments. This approach is purely data driven and bears the potential to elevate customer engagement since only relevant advertisements are sent to a particular customer or consumer. We must note that this mechanism also helps businesses to avoid a scenario wherein, a flood of ads is indiscriminately sent to customers of all hues, thereby boosting ‘junk’ email and missing out on valuable marketing opportunities. We must bear in mind that targeted advertisements offer a much higher return on investment because such advertising expands the scope of conversions by turning shoppers into buyers.

Insights gained from the use of marketing-focused predictive analytics can help brands and businesses to frame effective go-to-market strategies. This feature offers a compelling proposition to modern brands and businesses because it enables them to calibrate their marketing efforts in response to real time situations. For instance, a software manufacturer that designs and develops software packages for consumers at various price points can use predictive analytics to pre-offer software upgrades to a particular set of customers. This action is premised on information (gleaned from analytics) that indicates that said set of consumers is most receptive to upselling strategies and therefore, likely to react favourably to software upgrades. We could view this scenario as a situation wherein, a commercial vendor is using data sciences to target a specific segment of the market with certitude in an attempt to boost business outcomes.

Every customer is essentially different from the next and therefore, brands and businesses would gain by personalising product offers. The relevance of predictive analytics remains paramount because algorithms crunch the numbers generated by historical interactions between a customer and a business and generates recommendations for a particular consumer or customer. For instance, an online business operator that seeks to deepen the scope of customer engagement can deploy predictive analytics to generate recommendations for individual customers. The said technology can survey available data and enable the business to recommend products to individual customers, thereby effectively saving the customer time and effort (in terms of browsing and online search activities). The scope of customer delight is clear in such a situation and the business clearly posts a win when we view the outcomes through the lens of effective customer engagement.

Brands and businesses should take a pro-active stance in retaining customers in order to thrive in competitive markets. This is essential to every business, whether traditional or online. To that end, these commercial entities can use predictive analytics to convert hesitant customers into confident buyers. For instance, e-commerce operators need to deal with the serious consequences of shopping cart abandonment. This can be achieved by using relevant algorithms and the campaign can be followed up through emails sent to customers that abandoned shopping carts. We must note that the problem posed by cart abandonment, essentially, presents online businesses a novel opportunity to expand the scope of customer engagement. The said campaign can remind customers of their incomplete shopping expedition, while attaching certain inducements to expedite the aborted transactions. We note that this approach hinges on predictive analytics and can help business operators to recoup lost revenues, while creating enough scope for re-connecting to wayward customers.

Customer loyalty remains a critical outcome of customer engagement campaigns and strategies. To achieve that end, brands and business enterprises can harness the power of predictive analytics to boost customer loyalty. For instance, a business that is intent on expanding its customer base and developing customer loyalty can deploy predictive analytics to analyse historical customer behaviour and select a pool of customers based on said criteria. This set of customers can be trained to evolve into brand advocates and commercial evangelisers that promote the business and its merchandise in virgin markets. We must note that such campaigns are essentially time consuming and need careful stewardship before campaign managers can harvest the desired outcomes. That said, it is significant to note that predictive analytics was the core mechanism that set the said campaign in motion and this fact spotlights the use of such mechanisms in customer engagement campaigns.

In the preceding paragraphs, we have surveyed some of the uses of predictive analytics in boosting customer engagement. We must note that software algorithms and the deployment of data sciences are recent manifestations of the application of technology in the service of commerce. Therefore, brands and businesses should exert themselves thoroughly in exploring new ideas that expand the scope of analytics in customer engagement. The emergence of new technologies will make it possible for corporate planners to approach data from different angles and harvest novel information that was hitherto inaccessible. Therefore, corporate captains and business planners alike must acknowledge that the proliferation of data-intensive technologies will likely cast transformative effects on businesses and enterprises as a whole. In light of the above, commerce must utilise data in all its manifestations to overcome barriers and boost the possibilities of customer engagement.


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