Spend some time on any media channel—be it a newspaper, radio, social media, or any other Internet-based channel—and it won’t be long before you’re bombarded with ads trying to sell you a myriad of products. Brands spend a ton in such marketing efforts in a bid to get your attention and influence you into engaging with them. But how much of an impact are they making on you, the individual consumer, with these spray-and-pray tactics? Perhaps none at all. In fact, you may find such brand messages rather annoying.

Now, imagine receiving a highly personalized message on your mobile. The message not only recognizes you as an individual but also addresses your current shopping need. The brand, through this message, gives you an attractive offer on a product or category you’re interested in, and that too while you’re in a store pondering what to buy.

Which of the above two scenarios are you more likely to respond to? Obviously the latter. An Accenture study revealed that 91% of consumers are more likely to shop with brands that provide relevant offers and recommendations.

Online stores tend to enjoy greater success when it comes to personalizing shoppers’ experiences. With customer data at their fingertips, they can access granular insights related to every customer’s behavior, needs, preferences, and purchase pattern. This intel enables e-tailers to predict what a customer wants and make relevant offers and recommendations accordingly. This results in higher customer engagement and sales. And customers get the instant gratification that they seek.

Most offline retailers, who still rely on traditional marketing methods, aren’t as successful in delivering the same level of experience to shoppers. However, real-time in-store personalization can help them compete on an equal footing and craft delightful experiences for customers.

Understanding In-store Personalization

When an offline retailer starts identifying every customer as an individual, understanding their current context, and combining these insights with their historical behavior and tastes to deliver tailored recommendations, they have essentially started delivering in-store personalization.

However, all of this needs to be done in real-time while the customers are still weighing their options in a store. That would make it possible for retailers to make timely cross-sell or upsell offers that influence buying decisions and boost revenue. Retailers usually identify a shopper after they’ve reached the POS, but by this time the window of opportunity has long been shut.

But Is It Possible to Deliver In-store Personalization in Real-time?

The answer is yes, and lies in leveraging the great enabler – technology. A customer’s mobile phone and its Bluetooth capabilities along with the store’s WiFi and RFID tools can help offline retailers to achieve personalization that is nothing short of magical. Let us understand how from a few examples.

Scenario 1

Suzanne can access the free WiFi of a store on her mobile phone. Since her mobile number is already present in the store’s registry and linked to her past purchases, a buyer profile can easily be generated in advance that indicates what she normally shops for, when, and how often. It will also include data on if she chose a particular brand due to a sale or went with another despite an offer going on. This allows the store to send messages that are relevant to her.

“Hi Suzanne, running low on coffee pods? Do not miss an offer on Brand A in aisle number 3! Get up to 30% off on select products by mentioning your mobile number when you approach the biller.” Here, the system knows which day of the week Suzanne normally drops in to buy coffee pods and sends her a notification about an offer. It could even be made specific to address a particular flavor.

Scenario 2

Upon entering a mall, Jonathan gets a push notification to accept offers via Bluetooth. The store he frequents can then easily send him a personalized message to cross-sell another product related to his usual purchases.

“Good afternoon, Jonathan. Head over to The Gent. Get a 25% off on our brand-new selection of neckties when you buy any of our formal shirts/trousers. Use the coupon code GENTREGULAR at the time of billing.”

Scenario 3

Judith receives a push notification to send an SMS to a specific number so she can get personalized offers from a store. The SMS she sends enables the system to identify her as a first-time customer. The next SMS she receives is something along these lines: “Welcome to our store. Get 30% discount on anything you purchase with the coupon code THEFIRST.”

How Technology Enables In-store Personalization

All the aforementioned situations had the customer identified and sent relevant messaging that were targeted towards them. Doing all this in real-time requires analytics and machine learning that can work with enormous volumes of data from various sources.

Often, a rules engine in the background helps in identifying business conditions on moving and stationary data. Contextual targeting becomes easier when the retailer divides their customers based on demographic and psychographic analysis. Technology not only allows the retailer to drive personalization but also helps measure its effectiveness and improve upon the messaging as more and more data is collected.

Retailers can give their personalization strategy an added boost with mobile apps. Through apps, they can stay connected with customers and send them contextual messaging whether they’re inside or outside the store.

Beacon-based positional targeting has been implemented by many large retailers, within the store. Visual Light Communication (VLC) technology-based LED lights acting as beacons can interact with mobile phones, and they have seen increased adoption around the world.

Using beacons, retailers can even personalize based on the aisle a customer is in within the store. For instance, if a customer is in the wine aisle, they could receive an offer on their favorite red wine – let’s say, Merlot. And when the customer is in the pantry section, the beacon uses context to send another message recommending what pairs well with red wine, with offers for roasted Chicken.

Embrace In-store Personalization to Build Loyalty

In a world where customers are spoiled for choice, retailers should leverage technology to deliver relevant, contextual customer engagement. This is the key to building long-term loyalty and staying relevant in the market. What retailers shouldn’t do is spam a customer who’s trusted them with their information. Hyper-personalizing experiences throughout the entire customer lifecycle, across channels and touchpoints, is the way forward.

Check out our comprehensive omnichannel personalization guide to learn how to hyper-personalize customer experiences across both online and offline channels.

As businesses aim to adapt and sharpen their skills in the digital-first world, they need new abilities to create business success. As customers interact with businesses in a real-time, always evolving competitive environment, they have to implement decisions with adaptive precision and run algorithmic experimentation at granular scale.

Experimentation is the technique carried out under a controlled environment to discover an unexplored effect or principle; to put forward or to establish a hypothesis, or to demonstrate a known principle.

Hence it is very important to create a culture of experimentation within any organization. Experimentation does imply that ideas bloom but also run the risk of some failure, and companies should be prepared for it. Without failing at some ideas, one will never be able to achieve that single great idea that might take your organization to the next level of growth.

Experimentation is an age-old technique to test a hypothesis. A good example of this is retail catalogs, which adopted an experiment to see if prices ending with $0.99, i.e. $7.99 and $8.99, would generate higher sales. To explore the effectiveness of this experimentation, retailers sent out varying product catalogs and, in some cases, left the prices unchanged, and in others, they changed the pricing either at the beginning or at the end of the catalog with $0.99. To their surprise, they found that having such experimentations resulted in an overall increase in sales.

Lee Hibbett, an Associate Professor of Marketing at Freed-Hardeman University, says it might appear ridiculous to price products one cent short of a dollar, but this trick of pricing has a psychological influence on customers. Hence as per Hibbett, because we read from left to right, the first digit of the price resonates well with us most often; hence the reason customers are more likely to buy a product for $7.99 than the same one for $8. This is famously called psychological pricing or charm pricing.

Retailers also use bundle pricing strategies, wherein organizations sell a set of goods with lower prices than they would have charged if the customer bought all of them independently. Most common examples are BOGO (buy one get one free), or buy two products and get another product at 20% off. Pursuing a bundle pricing strategy allows retailers to increase their profit by giving customers a discount.

To get to the optimum products that can be experimented as part of the bundle pricing strategy, retailers rely upon affinity analysis, a.k.a. market basket analysis. The main idea behind affinity analysis is to achieve insights by identifying which products are frequently purchased together. It helps retailers find patterns between purchases in orders, which can be used as a cross-selling opportunity. It can also be used to ascertain what products can go on discount. It’s needless to emphasize that this can apply to many other use cases, which can help increase sales and customer satisfaction.

Another strategy is anchor pricing, which allows retailers to make a product appear cheaper when it is put alongside another product. It is a technique to keep a buoyant pricing strategy. This is the very reason retailers put their own private labels or their own brands next to the market leader, as most often their own brand is cheaper than major label brands.

Price experiments are a technique used by retailers to gauge the association between demand and price change. Price experiments enable retailers to not only present the best price to their customers but also make sure they are rewarded well from a strategic business standpoint.

Tech companies have embraced the experimentation techniques more often than others. Online experiments are vital for ecommerce companies in the development of their web facing products. Given that they have a large user base, even tiny improvements can have a large impact on their profits. For such organizations online controlled experiments are crucial for assessing the impact of product changes in their businesses. They represent the best scientific design for establishing causal association between changes and their influence on observable user behavior.

This example demonstrates how important it is to assess the potential of fresh ideas. A Bing team member of Microsoft in 2012 suggested changing the way Bing search was displaying advertisement headlines, but his product manager considered it a low priority item. It was not until six months later that another team member launched a simple online controlled experiment of an A/B test to assess the impact of it. To everyone’s surprise, within a few hours it was delivering abnormally high revenue.

Similarly, Google’s 41 shades of blue experimentation demonstrates that small design decisions can have considerable impacts on user engagement, resulting in substantial positive engagement.

Digital marketing plays a pivotal role in generating measurable transactions that can be measured to calculate a return on investment. Amazon found that every 100ms of latency cost them 1% in sales. Similarly, a brokerage firm found that if they are 5ms behind their competition then they could lose $4 million in revenues per millisecond.

Thus, businesses are starting to appreciate how critical it is not only to run as many experiments concurrently — but also as cheaply — as possible. Now digital marketing plays a pivotal role in generating measurable transactions that can be measured to calculate a return on investment.

The other decision is the timeframe an experiment has to run for. In general, it is recommended to run an experiment for one to two weeks. Hence the treatment effect measured for such a time frame is called short-term impact. But there are cases when long-term impact is a lot different than short-term impact. For example, increasing the price of products on an ecommerce platform might increase revenue in the short term, but eventually act against long-term revenue because users might shift to some other site for better price.

Experiments might fail due to various reasons, for example the tactics being inadequate. If the experiment fails, then there could be a problem with the underlying experiment design, infrastructure, data or analysis of the result.

Leading global companies competent in algorithmic customer engagement are now powering digital strategies through their products and have always nurtured experimentation as a de facto standard. These companies have embedded experimentation into their organizational DNA, for all their deployment strategies, be they DevOps or MLOps adapts Blue/Green, Canary, A/B testing technique, while rolling out new feature functionalities.

They leverage multivariate testing to evaluate the impact made by their recommendations, merchandising rules and location of one or more placements on certain page(s) on their customers’ ecommerce platform. Likewise, they are also leveraging Contextual Multi-Armed Bandit to intelligently learn from multiple contexts like browsers, user segments, products and handheld devices, for true real-time decision-making supporting personalized recommendations in a seamless and automated fashion.

(This article has only focused on experimentation and how experiments influence decision making processes in the digital-first world and a few techniques that are adhered to in industry. It is in no way a full-fledged representation of various approaches that are followed across the industry since this is an evolving space.)

The article first published in RetailTouchPoints