You already know that data is a gateway for retailers to improve customer experiences and increase sales. Through traditional analysis, we’ve been able to combine a customer’s purchase history with their browser behavior and email open rates to help pinpoint their current preferences and meet their precise future needs. Yet the new wave of buzzwords such as “machine learning” and “AI” promise greater accuracy and personalization in your forecasts and the marketing actions they inform.
What distinguishes the latest predictive analytics technology from the traditional analytics approach? Here are three of the numerous examples of this technology’s impact on addressing retail challenges and achieving substantial ROI.
1. Increase customer lifetime value.
Repeat customers contribute to 40% of a brand’s revenue. But how do you know where to invest your marketing dollars to increase your customer return rate? All of this comes down to predicting which customers are most likely to return and factors that influence the highest customer lifetime value (CLV) for these customers, which are both great use cases for machine learning.
Consider this example: Your customer is purchasing a 4K HD TV and you want to predict future purchases. Will this customer want HD accessories, gaming systems, or an upgraded TV in the near future? If they are forecasted to buy more, which approach will work to increase their chances of making the purchase through you? Predictive analytics can provide the answer.
One of the primary opportunities is to create more personalized sales process without mind-boggling manual effort. The sophistication of machine learning algorithms allows you to quickly review large inputs on purchase histories, internet and social media behavior, customer feedback, production costs, product specifications, market research, and other data sources with accuracy.
Historically, data science teams had to run one machine-learning algorithm at a time. Now, modern solutions from providers like DataRobot allows a user to run hundreds of algorithms at once and even identify the most applicable ones. This vastly increases the time-to-market and focuses your expensive data science team’s hours on interpreting results rather than just laying groundwork for the real work to begin.
2. Attract new customers.
Retailers cannot depend on customer loyalty alone. HubSpot finds that consumer loyalty is eroding, with 55% of customers no longer trusting the companies they buy from. With long-running customers more susceptible to your competitors, it’s important to always expand your base. However, as new and established businesses vie for the same customer base, it also appears that customer acquisition costs have risen 50% in five years.
Machine learning tools like programmatic advertising offer a significant advantage. For those unfamiliar with the term, programmatic advertising is the automated buying and selling of digital ad space using intricate analytics. For example, if your business is attempting to target new customers, the algorithms within this tool can analyze data from your current customer segments, page context, and optimal viewing time to push a targeted ad to a prospect at the right moment.
Additionally, businesses are testing out propensity modeling to target consumers with the highest likelihood of customer conversion. Machine learning tools can score consumers in real time using data from CRMs, social media, e-commerce platforms, and other sources to identify the most promising customers. From there, your business can personalize their experience to better shepherd them through the sales funnel – even going as far as reducing cart abandon rates.
3. Automate touch points.
Often, machine learning is depicted as a way to eliminate a human workforce. But that’s a mischaracterization. Its greatest potential lies in augmenting your top performers, helping them automate routine processes to free up their time for creative projects or in-depth problem-solving.
For example, you can predict customer churn based on irregularities in buying behavior. Let’s say that a customer who regularly makes purchases every six weeks lapses from their routine for 12 weeks. A machine learning model can identify if their behavior is indicative of churn and flag customers likely not to return. Retailers can then layer these predictions with automated touch points such as sending a reminder about the customer’s favorite product – maybe even with a coupon – straight to their email to incentivize them to return.
How to Get Started
Though implementing machine learning can transform your business in many ways, your data needs to be in the right state before you can take action. That involves identifying a single customer across platforms, cleaning up the quality of your data, and identifying specific use cases for machine learning. With the right partner, you can not only make those preparations but rapidly reap the rewards of powering predictive analytics with machine learning.
Want to learn how the 2nd Watch team can apply machine learning to your business? Contact us now.