Jaw-dropping increases in computing power, data collection, and data storage over the past decade mean predictive analytics software can now offer quantifiable and immense benefits to almost any area of life and business, including eCommerce and its marketing strategies.
Put simply, the science of data analytics is that which develops analyses of raw data in order to reach conclusions. These conclusions can be used in informing, judging, and decision-making, or combined with other analytics to reach more and other conclusions. The use of analytics on its own is only useful or appropriate in some circumstances; human intelligence and fine judgement cannot entirely be replicated. But when humans and artificial intelligence (AI) work together, they learn from each other, and the actions of both can be improved exponentially.
Predictive analytics, then, gathers data, and employs it to support accurate forecasts of future outcomes, enabling sound present human decisions. It uses data to tell you in the present how to bring about the best possible future.
eCommerce companies can take such customer data as previous purchases, items bought by other customers with a similar purchase history, and items the customer has browsed or reviewed, to determine what else they might want. They then use this information to create next purchase recommendations and other forms of hyper-personalization that will please their customers.
The example above demonstrates one way predictive analytics can benefit customers. ‘You might like’ suggestions can bring products to their attention that they’re likely to enjoy, maybe products they didn’t even know existed, and save them time searching through things they don’t want. But a predictive analytics software also benefit eCommerce companies and their marketing strategies.
1. Time is money. Marketers don’t have to gather and analyse data when a predictive analytics software can do it for them, almost unquantifiably faster, and more accurately. Data is information, but through AI it becomes insights.
2. By using prediction to determine marketing methods and to analyse which were most successful, budgets can have better focus on what works, reducing spending on what doesn’t and pushing up ROI.
3. Marketing traditionally meant onerous research, design, and outreach. Predictive analytics software enables strategically focussing resources on the right people at the right time.
4. Online customers have come to expect hyper-personalization of their interface, and predictive analytics make this frictionless for both marketers and end-users.
The availability of real-time data enables the ongoing delivery of ever-more relevant service, content, product information, and branding elements to each individual browser and purchaser.
These benefits and others lead to more efficient, cost-effective marketing strategy, more quickly, giving marketers higher confidence and timely, quality feedback.
Return On Investment (ROI) is the key metric by which the profits generated by a specific marketing action can be measured. Anything that increases marketing effectiveness and therefore ROI is a must-have, and AI is now such an asset. As the popularity of predictive analytics grows, so does its usefulness.
The approaches and recommendations to customers described above look little different from the one-to-one experiences customers enjoyed fifty years ago in their favourite shop. The clerk knew their name, tastes, and budget, and this familiarity was a pleasure. It kept them coming back, and spending more, loyal for life.
This is of course exactly the relationship you want with eCommerce customers. And you can create it, using predictive analytics software to provide hyper-personalization of their experience. Personalised suggestions, offers, thanks, and other communications make customers feel special, make interactions more meaningful. These improved connections mean marketers can then better discern whether those customers are becoming more likely or less likely to buy again, so can tweak strategy and thus ROI. Hyper-personalization also means customers are less likely to return items they purchase, giving the company yet another competitive advantage.
A specialised form of software called Customer Data Platforms (CDPs) brings together advanced analytics, artificial intelligence, and machine learning to provide real-time 360° views of and predictions about customers. It stitches differing sources of data together and puts it into the hands of marketers. In being able to both ‘see’ and differentiate between customers, i.e. customer segmentation, marketers can act immediately to provide an optimum experience.
Predictive analytics streamline lead generation by removing old school guesswork. Leveraging data provided by machine learning algorithms enables you to better predict which customers are genuine and ideal, are the most ready to buy, and have a larger spend. Strategy targeted at high-value customers creates great ROI, optimising the aggregate marketing mix and marketing spend.
Analytics allow for swift identification and scoring of leads. Differentiating leads gives marketing and sales a way to collaborate more effectively. Using less time working out who’s who through segmentation leaves more time to do what it takes to close new deals, and give customers a great journey that will bring them back.
It also allows you to leave alone those customers who might be put off by too many or the wrong approaches. It’s as important to know who doesn’t want to be approached as to know who would benefit.
Lead scoring is preceded by lead nurturing. Data on who makes the best leads can be used to inform sales and marketing strategies sooner. Content and ad campaigns can be tailored to draw different segments of customers into and along the funnel.
Each campaign and strategy better informs the next when underpinned by predictive analytics and feedback from their ROI impact. When you’re learning which channels of which content succeeded with specific segments, you can continually further refine content creation and platforming. Superior communications mean more likely conversions, in a virtuous circle with improved ROI each go-around.
The rate of customer attrition, known as ‘churn’, can be measured after the fact, but by then it may be too late. A company can only grow if its retention rate is higher than its churn rate.
A predictive machine learning algorithm can work to identify which customers are most likely to churn. This allows swift intervention to nurture that customer through such dynamic pricing strategies as personalised offers and discounts.
With every retained customer, loyalty grows. Hyper-personalization through a predictive analytics tool lets customers be shown they are valued. Engaged customers become loyal. Loyal customers not only keep returning to buy, but their spend tends to be higher: a win-win for all, building your business together.
You’ve seen some ways eCommerce marketers can use a predictive analytics software in their strategies to increase ROI with other onward ripples of multiple added values. The problem for commerce used to be lack of or inaccurate data. But the amount of captured data in the world went from 2 zettabytes in 2010 to 59 zettabytes in 2020. With the right software, smart data handling leads to information which leads to insights, rich veins of gold for eCommerce marketers to mine, making decisions based on those conclusions and acting on them quickly, then reviewing outcomes and recalibrating strategy.
Shimoku’s Retention and Demand Planning suites have been developed to deliver exactly these many benefits. With so much data, and more captured daily, marketers can no longer rely on human brains and working hours to take advantage of it. We need advanced marketing tools and measurement capabilities in the form of predictive analytics. Computers, commerce, and customers can now work together for the most enjoyable journey possible.