Reading the minds of consumers is an incredibly difficult concept to grasp. In the old days, traditional marketers tried a number of strategies to understand customer mindsets through things like focus groups, surveys, in-store promotions, displays and offering products at different price points. Even though these approaches were relatively beneficial, there was still a lot left to the unknown.
These days, predictive analytics are like a crystal ball for marketers. Advancements in this field are a win for both businesses and software providers. In fact, Research and Markets has forecasted that the market for predictive analysis will top $9.2 billion by 2020, up from $2.7 billion in 2015.
Perhaps the most mainstream example of predictive analytics is used by Amazon. For instance, each time a visitor opens the homepage, there is a long list of suggested products based on previous activity.
As a result, this recommendation engine accounts for up to 30 percent of their revenue.
Predictive analytics is just the tip of the iceberg in turning e-commerce business owners’ dreams into reality. Let’s discuss three major insights to take away from this futuristic marketing concept.
Accurately Targeted Promotions
Promotions are the lifeblood of retail businesses both online and offline. While they seem fairly simple to create, doing them right can be rather difficult.
Regardless of how smart a promotion is, if it is not properly targeted, the appeal will inevitably be a lot lower. Seventy-two percent of respondents to a PwC report listed targeting and segmentation as the most important elements of their customer strategy merchandising strategy; however, market research and studies continue to find that the majority of merchants are not satisfied with the current tools they use for promotion.
The beauty of predictive analytics is that it correlates data from an array of sources to personalize a campaign for a specific customer segment within the target market.
Macy’s has been doing a phenomenal job of implementing predictive analytics into its e-commerce marketing efforts. Using a solution from SAP, its strategy is targeted to registered users on the website. Similar to Amazon, Macy’s gathers data from previous browsing activities within its vast product listings and sends targeted emails with relevant promotions to each customer segment. Since it implemented predictive resources, Macy's saw a 12 percent boost in online sales.
Sounds easy right?
One of the biggest misconceptions about promotion based on predictive analytics is that it is simply a plug-and-play tool, where you submit data and revenue magically pours out. Successfully implementing predictive analytics into a promotion requires a firm understanding of the concept with clear goals in mind and the ability to decipher the valuable data from “junk data.”
Keep in mind, each business has different needs and customer profiles. There will more than likely be a good deal of trial and error. Be prepared to make frequent changes and test the results.
Optimized Inventory Management
Predictive analytics can be a game-changer for optimizing threshold inventory. For instance, when a predictive model detects an increase or decrease in sales for a particular item, it will alert the business and minimize the required stock amount so retailers are able to allocate their funds to buy products in greater demand to ultimately increase profits.
Big data has the potential to completely revolutionize inventory management. Problems in relation to out-of-stock (or overstock) after a high-selling season can be issues of the past. In turn, if the right data is pulled and properly analyzed in terms of trends and consumer preference, you will discover a new world of efficiencies.
Walmart is a prime example of using predictive analytics to influence inventory management both online and offline. With suppliers in over 70 countries and an average of over 175,000 products stocked per store, aggregating data on every single aspect of the retail operation is crucial in properly examining customer buying patterns to forecast demand.
Walmart collects streams of data on a daily basis from both online and in-store tracking systems that it feeds back into its supply and distribution platforms. By analyzing and combining the information from local demand and sales forecasting, Walmart is able to minimize product shortages and stocking issues across the board.
“Optimizing the inventory in our system is a huge lever to better serve customers and take out a lot of cost, not only though transportation and handling but also through markdown elimination,” said Doug McMillon, President and CEO of Walmart.
This concept of inventory management is by no means restricted to retail. For example, Netflix, who charges customers a flat rate, utilizes big data and recommendation engines to drive usage. Remember Blockbuster? Before 2010, it had over 60,000 employees compared to Netflix’s 2,000.
Blockbuster’s inability to effectively forecast demand resulted in a significant loss of share to Netflix, who can accurately predict customer consumption patterns and plan selection accordingly.
Using recommendation engines to efficiently predict demand for both inventory and the big picture can give brands a big leg up in driving sales (or in Netflix’s case, loyalty).
Yes, B-to-B e-commerce is a thing. And yes, lead-scoring is important for e-commerce marketers, too. Better lead-scoring is perhaps one of the most significant advantages predictive analytics presents. One of the biggest challenges for a sales team is knowing what exactly qualifies a strong lead. According to Gleanster Research, only 25 percent of leads are worthy of being pursued for an immediate conversion.
With predictive analysis, when someone visits your website, algorithms gather data on factors such as social information, demographics, behavioral patterns, and much more to determine whether or not the lead is worth chasing.
With more qualified leads at stake, the need for effective communication between marketing and sales is more prevalent than ever. In this scenario, any lapse in collaboration is much more likely to result in a loss of revenue. Therefore, predictive analytics and efficient project management go hand-in-hand.
Ninety percent of B-to-B organizations find predictive analytics more valuable in lead-scoring than traditional approaches. In the end, you will save significant resources and manpower when pursuing potential customers backed by predictive analytics.
Predictive analytics make marketing much quicker and more efficient. It never hurts to go back to the drawing board and critically examine your goals in terms of target audience, savings opportunities, and revenue uplift to find a predictive solution to properly address and meet your needs.
Rohan Ayyar is the regional marketing manager for India at SEMrush. His blog, The Marketing Mashup, covers digital marketing from the perspective of B2B, B2C, lead generation, mobile marketing, SEO, social media, content marketing, database marketing including predictive analytics, and conversion rate optimization. In addition, he'll look at emerging marketing technology and how marketers can use it. Reach Ayyar at email@example.com.