The famous Walmart data mining experiment which combined point of sale data and loyalty program data already exemplified several years ago, the significance of a multidimensional customer view and understanding. The experiment showed that male shoppers on Friday afternoons who came to buy diapers also bought beer. This experiment became the Northern Star for layout customization and considerations. Naturally, once this data is combined with digital footprints of our customers, the realm of possibilities becomes infinite and in online stores, the ability to provide customized and more profitable shopping experiences becomes ordinary practice.

This is one of the more interesting areas of retail marketing that AI and deep learning are trying to tackle. What is the actual buying route (some call it customer journey) shoppers take to make a purchase and how can that best be influenced by suppliers and retailers? AI can help marketers understand what impacts a shopper’s decision to move ahead with a purchase, abandon it totally or put it on a wish list by comparing successful and unsuccessful buying route given the different loyalty, promotion, advertising, pricing and merchandising tactics used. Then those positive influences can be accentuated and the negative ones removed to optimize the process and simultaneously increase shopper engagement (both direct and indirect) and drive profitable sales. One of the primary drivers of business growth that are not based on transactional behavior is new customer acquisition. AI plays a huge role in identifying the relevant customers that are likely to buy something in a given store. Luring them in, using effective channels of marketing and targeting and identifying their buying behaviors and journeys once they interact with the store is the one of the more interesting areas of retail marketing that AI and deep learning are trying to tackle. What is the actual buying route shoppers take to make a purchase and how can that best be influenced by suppliers and retailers? AI can help marketers understand what impacts a shopper’s decision to move ahead with a purchase, abandon it totally or put it on a wish list by comparing successful and unsuccessful buying route given the different loyalty, promotion, advertising, pricing and merchandising tactics used. Then those positive influences can be accentuated and the negative ones removed to optimize the process and simultaneously increase shopper engagement (both direct and indirect) and drive profitable sales.

Transparency is crucial to any AI effort by retailers or brands. The minute shoppers think you’re trying to pull a fast one with their personal data, trust starts to erode. Conversely, if you’re upfront with the customer and present offers that help them shop better – that could mean price, value, assortment, service and more, depending on the individual or segment – then you can quickly build trust. Bottom line is adhere to the Golden Rule and you likely won’t go wrong.

As more and more data is collected on shoppers, potential shoppers and the products themselves, more and more analytics can be conducted to determine when a buying funnel has actually begun or is about to, which marketing and merchandising tactics work and which don’t per buyer. We all know that one size does not fit all but the true way of adjusting the strategy relies on our ability to understand shoppers, their interests and their needs, and then target them when and where its most effective. AB testing becomes integral to decision making and is easily and seamlessly conducted for experimenting with new ideas and for finding the right targeting channels for the buyers. 

Retailers are getting better at influencing e-commerce shoppers, but there is still plenty of opportunity, even for the likes of Amazon and Walmart.com. AI is the best technology now available to define shopper personalities and help segment then target them. By collecting specific, publicly available data streams from social media and integrating that information with proprietary sources from point of sale or loyalty programs, retailers are better defining their value proposition to shoppers. This results in an ongoing conversation between the retailer and customer that both extends the shopping experience well beyond the site and sets up the platform for marketing moving forward.

Effective personalized email campaigns are achieved by creating the right combination of three key elements: 1.) personalized subject lines, 2.) personalized product recommendations based on personal taste, need, price sensitivity and market trends and 3.) a triggering event. If we send out emails with generic subject lines we probably won’t get the open rates we need even if the products we recommended are highly personalized. If we only personalize the subject line but the products that are being recommended are not personalized, we will lose the trust of our subscribers and they will stop opening the emails over time. Of course the third element -- the triggering event -- is a key factor in how many email our subscribers are actually getting. If we send too many emails, no matter how personalized, they will become redundant and will annoy subscribers.

Not all ecommerce stores have the computing power and amounts of data Amazon, Facebook and the likes have. Tech companies offering deterministic unified data that enables the true capture of purchasing journeys and allow for omnichannel marketing will be the leading force in helping retailers make the ultimate leap of truly understanding and knowing their customers. It could be that Facebook re-targeting campaigns are popular today, but what happens if most of your shoppers are NOT ON FACEBOOK? By truly understanding your customer, and having the insights relating to their browsing behaviors (not using cookies), retailers will not only identify the user across different platforms, they will be able to target the users with the right products for them, at the best distribution channel per user.

Millennials and Gen Z are the most transparent generations ever. They wear their demographics, likes and dislikes, social status and more on their digital sleeves. Simple mining of publicly available data will not only help retailers (and other businesses) better understand the differences between Millennials and Gen Zers, it will help them customize marketing efforts each group and their sub-segments. While actually talking to members of these generations is better than not talking to them, there is more accurate data and better insights to be found in the social spheres.

A.I. is the best technology now available to define the personalities for brands and help segment targeted customers. By collecting specific publically available data streams from social media and integrating that information with proprietary sources from brands and their retail partners, companies are better defining their value proposition to shoppers. This results in an ongoing conversation between brand and customer that both extends the shopping experience well beyond the store or site and establishes the platform for marketing moving forward.

There is a battle going on with bot-generated content in retail and beyond. As soon as companies and organizations put the latest protections into place, bad guys come up with something new. When one retailer realized that bots never mentioned the company’s name and weeded out those reviews that didn’t have it, a hacker came up with a solution to incorporate the name. Fortunately, some companies have gotten smarter and are setting up defensive measures that not only address the immediate problem, but make it worthless for the bots to attack.

The potential for these types of programs rests almost entirely on the back-end engine that drives the merchandising to the shopper. If it is just trying to match what the customer does in-store, it’s really nothing special and the shopper might as well go to the physical store. If it works with the shopper’s buying history and combines that with publicly available social media data to customize the experience for each person, then they may be on to something. Cross referencing multiple data sources from onsite activity, product popularity, hot products in the market and offsite data, and then utilizing this knowledge to truly understand who the customers are and what price sensitivities and considerations influence their shopping behaviors would enable a profound wholesome experience. 

They key to loyalty programs going forward is to match the interests of the customer more precisely with the potential rewards provided for being loyal to the store, site or brand. And the key to matching those interests with rewards is collecting data on the customer’s buying habits and desires from transaction logs and social media outlets. Only by truly understanding the environment each customer lives, works and plays in can a retailer or brand extend the kind of offers that will drive loyalty.

Big Data can take a lot of the art out of marketing, which may be good or bad. On the plus side, Big Data quickly validates what’s working and what’s not to decrease resources chasing failing ideas. On the negative side, it may inhibit new demand creation ideas that haven’t tested well in the past but may now be appropriate for the marketplace. Also on the downside, Big Data can cause retailers and brands to over-complicate their strategies, confusing or even scaring away would be consumers with too much info on themselves and their purchases. As for the last questions, surveys are only more valuable than Big Data sources when there are no Big Data sources, which is rarely the case now.

All retailers can always do better with conversion rate optimization. If they ever stop trying, some competitor is going to come along and grab market share. Conversion rate killers come in two groups -- human and technological. On the human side, customer service throughout the shopping experience can be hugely positive and hugely negative. Cashiers, clerks and others impact sales every hour -- or even more frequently. Properly trained and equipped with both the right data and the right equipment, these front-line staffers can drive sales as well as, if not better than, other shopper influencers. On the tech side, having the right actionable information at the right place and the right time for those employees to better engage the shopper is going to drive conversions. Conversely, the lack of the right resources at the point of purchase will almost certainly lower conversion rates.

There is a smell test that consumers conduct when dealing with new sites and stores, and even with new offers and technologies that are deployed, whether consciously or not. They ask questions like, can I trust this company with my financial information? Will they protect me from harm, and are they really interested in me as a customer? Retailers need to balance on a pretty thin line between engaging the shopper the way they think she wants to be engaged and being too in-your-face with her. It’s not easy but there are a lot of hints on what can work effectively (and not work at all) with digital personalization already widely available from social media sites and loyalty data. Just don’t cross that boundary of being at all questionable in your intent or execution. At the end of the day, if spot-on recommendations and easy purchase channels are saving the consumer lots of time and effort plus making them feel like they really got a deal on something they truly want, they won’t care much about being recognized across devices.