It’s no secret that technology is revolutionising consumer and brand interaction.

In most cases, consumers are changing their behaviour faster than most retailers can adapt their marketing strategies. Marketers must engage savvy shoppers across a plethora of channels, the competition is intense, and customer satisfaction and retention have become top priorities for most brands.

In our modern era, it is imperative to know and UNDERSTAND who our customers are , what they like/dislike, what will motivate them to buy or buy again, and why they leave.

It is vital to have a forward-looking approach and to predict answers to questions such as: “What will my customer be interested in next week? In which city is my customer likely to shop? What is the most effective channel to connect with customers when they are ready to buy? Which products are my prospects waiting for?”

Marketers have amassed an impressive amount of data on consumers’ behaviour and channels of choice, in which basic analytics have been translated into customer profiles and preferences pages. Using these historical patterns and behaviours, marketers have then built segments and categories to anticipate potential actions in the future.

But are such correlations accurate and dependable? Under some circumstances, it can be enough to launch a targeted campaign and deliver positive results. However, while specific messages can be relevant at a particular point in time during a shopper’s journey, they often take a broad view of who the customer is and what promotions will work for them and lead to a purchase.

A shopper may visit a website, search and browse items, then drive to a retailer’s store. There, they could share photos of a product on Facebook, interact with a brand’s app, then use their phone to add the item to their cart on the mobile site. Should these actions result in a cart reminder email? A retargeted display ad? Product recommendations on the website or other websites? A store abandonment email? An in-app push notification?

A ‘past predicts the future’ methodology will not offer a transparent approach for gaining customer loyalty Determining the optimal message for a single shopper is a time-consuming exercise. So, how could this possibly be done for all of your shoppers? Clearly, relying on a ‘past predicts the future’ methodology will not offer a transparent approach for gaining customer loyalty.

The answer is that marketers need to move to the next stage and shift to a more predictive model built on technology. With smarter analytics, automated marketing platforms and machine learning algorithms, the task might be easier than most currently imagine.

Boost Your Agility

Machine learning is the art of teaching an analytics solution to become more intelligent through advanced mathematics that feed into the solution. With constant use it becomes smarter – learning and evolving throughout the customer lifecycle with each and every transaction. This includes analyses derived from page views, checkouts, add-to-cart events and search queries on a website. Customer interactions with thousands of products are processed in near real-time – giving instant, individual recommendations for each stage of the buying process with every page refresh.

With machine learning technology, brands gain the ability to process diverse, and at first glance, random, behavioural data to understand how customers behave. Leveraging these tools, marketers need to become agile to successfully connect with their customer. They can launch customer intelligence-driven campaigns, reaching their targeted prospects with the right offer wherever they are located, and on the right device.

This improved customer insight empowers the seller to make better-informed decisions. It also enables the automation of marketing activities currently required to engage customers and inspire an order. Essentially, marketing strategies can move beyond the traditional concepts of a segmentation strategy to a more automated one, which truly speaks the customer’s language.

Customer Experience Is Key

Shoppers are overwhelmed with marketing messages everywhere they turn, but seldom do these messages engage the individual in a meaningful, relevant way. With machine learning, retailers can create compelling, personalised messages for every customer, building their loyalty and raising sales.

These technologies are crucial because having a good product is simply not enough – providing the best experience is key. With that, marketers need to present products in an appealing, relevant way. They need to take the customer experience to the next level to ensure shoppers will come back for more, over and over again.

All that’s left is to answer one question – are you ready to improve your customers’ lives with an unparalleled shopping experience?