Black Friday is just around the corner, and while for many retailers it’s the most wonderful time of the year, for others it’s the most anxious. Websites and physical stores are raising their optimization game, seeking to direct the spike in customers to their own outlets. Those who aren’t sure how to compete are set to lose out.
Just look at last year’s results: on Cyber Monday 2019, digital revenues in the US soared by 14% to $7.2 billion. Adobe Analytics forecasted total consumer spend for Black Friday weekend at a cool $29 billion. But while many stores (both online and bricks-and-mortar) performed well, those that didn’t read the mood right actually lost business. To make sure retailers are in the first category, they’ll need machine learning (ML) on their side (and their site).
How does machine learning help retailers?
From the moment visitors arrive at a store, they need to be served up exactly what they need and in a way that makes them want it. That may sound tricky, but ML helps retailers do just that, in three key ways:
Create a dynamic, personalized experience — even for first-time visitors
Competition is increasingly fierce between brands and stores to capture and keep the attention of online shoppers. With so many great deals and sites to choose from, visitors can afford to let their attention dwindle if they don’t spot something that grabs them in the first few seconds. That makes state-of-the-art personalization an absolute must. Ideally, each potential customer that lands on an ecommerce site should feel like a personal shopper has handpicked and presented the most relevant deals for them, tempting them to make a purchase with minimal clicks.
This is only possible if sites really understand the visitor enough to predict their preferences and behavior accurately and instantly. And that’s a lot easier when ML is combined with the right external data sources. This should help get a quick, complete perspective on what this visitor is looking for, where they’ll click, and what additional products they are likely to purchase if recommended next.
Even better, by incorporating ML-based customer scoring models, retailers can also predict the value of each customer, so that they know whether securing a conversion is worth the effort and resources in the first place. Since they can’t create infinite versions of each site, this gives them a way to prioritize who they are personalizing the site for.
In other words, building ML into personalization efforts helps them to convert sales quickly, while also giving them a strategic roadmap for honing in on the highest-value potential conversions, too.
Target exactly the right audiences
Driving customers to a website is the first priority, but retailers also want to make sure they get high probability clicks. ML targeting models give them a great way to make every dollar spent on ads and social count with smarter targeting of the right shoppers.
With machine learning, they can do all of this better and faster. Like all types of AI in marketing, ML gives them ways to improve and automate complex, vital tasks like lead scoring, lead nurturing, and social media retargeting. ML models provide a means of translating core KPIs into data science questions that can be answered with the right customer and external data.
In other words, with ML, retailers can make incredibly accurate predictions about a prospective customer’s lifetime value (CLTV), return on marketing investment (ROMI), likelihood of canceling their subscription, and a thousand other factors before you decide whether to commit a cent to showing them an ad. Once they’re in the marketing funnel, retailers can use a different set of ML models to predict how they will respond to certain types of email campaigns or content, ensuring they match the right content to the right customers all along the chain.
By supplementing knowledge of which products a person has browsed in the past with what is known about their broader interests (and insights on industry trends), social media ads can be retargeted in subtle, intelligent ways that actually work.
Get the ball rolling on direct mail campaigns
Yes, more people are doing more of their shopping online, and the approaching holiday season is likely to be dominated by digital. But that doesn’t mean that no one shops in person anymore. Retailers only have to watch last year’s news footage of Black Friday crowds fighting tooth and nail for the last cut-price TV to remind themselves how fierce in-store demand can get.
Many people still prefer to pick up a bargain with their own hands, and physical stores remain a major pull. This makes classic marketing strategies like coupons and promotion vouchers as important as ever. However, these can be expensive for retailers and marketers to deliver.
ML is invaluable for retailers looking to reduce the cost of direct mail campaigns. Careful modeling helps them pinpoint exactly the right recipients so that they can increase sales without wasting time and money contacting people who simply aren’t interested in these channels.
Some of Explorium’s customers have seen amazing results by incorporating ML into their direct mail optimization strategy. One retailer, in particular, had been struggling with a 1% response rate to their direct mail campaigns until they switched to using ML-driven data enrichment tools to build a much smarter optimization model.
This new model incorporated insights from census data, previous purchase data, online activity, and tech-orientation scores. It gave the retailer a far more nuanced, accurate picture of which customers were likely to respond positively to direct mail marketing, and as a result, they were able to narrow their focus to higher-probability conversions.
Not only did they see an immediate 23% jump in responses, but by focusing their efforts and streamlining their costs, they increased ROI by 14% in a matter of weeks, too.
Final thoughts: maximizing a great opportunity
In a climate like this, nothing is guaranteed, and last year’s stats show that retailers still need a well-thought-out strategy to eke out from this potentially lucrative time of year. Black Friday and Cyber Monday offer an enormous opportunity, but without data-driven marketing, it might just slip through their hands.
Explorium offers a first of its kind data science platform powered by augmented data discovery and feature engineering. By automatically connecting to thousands of external data sources and leveraging machine learning to distill the most impactful signals, the Explorium platform empowers data scientists and business leaders to drive decision-making by eliminating the barrier to acquire the right data and enabling superior predictive power.