Picture this…

You’re browsing online at work for a pair of shoes and on your way home you decide to stop in the shoe store, try them on and buy them. For the next 6 weeks, ads about the same pair of shoes follow you around the internet, from news site to social networks, reminding you about the shoes you “haven’t purchased yet”. These ads are annoying, completely unhelpful and irrelevant. Sound familiar?

Now picture this…

You’re at work making your grocery list using your local grocery store’s mobile app. Without realizing you forget to add the Greek yogurt you’ve run out of, which you have purchased every week for the last year. Later that evening on your way to the store, you get a notification to your phone that Greek yogurt is on special today. You’re thrilled to have the reminder AND to have a discount. With a swipe of your thumb you add it to your list and look forward to tomorrow’s breakfast.

Both scenarios use personalized marketing to remind you about a purchase, yet one is exceedingly more relevant and valuable than the other.

There is a fine line that retailers must walk when using customer’s personal data for marketing. That line falls between using data to very obviously push more sales (sometimes at the detriment of pissing off your customers) and using data to create a positive shopping experience based on individual shoppers’ preferences.

While the former might provide fast sales, the latter is a long-term investment in your customers that will lead to increased loyalty and spend over time.

So how can you be sure your personalization efforts are creating positive, helpful shopper experiences and long term customer loyalty?

1. Use real-time analysis & machine-learning algorithms

By analyzing your customers’ behaviors in real time you will be able to provide the most up-to-date relevant messaging for each customer. For example, if you know that a customer is in your store and what products they viewed online that morning, you can provide them with specific offers on those specific products during their immediate path to purchase.

By using machine-learning algorithms you can identify products that are similar to each other, and then use this information to provide relevant recommendations based on customer’s unique preferences. For example, if you know that a customer tends to buy gluten free products, you can notify them when a new gluten-free product is available in your store.

2. Connect all data sources to create a single view of the customer

By connecting all of your separate data sources — loyalty program, eCircular, eComm — you can create a holistic view of each customer across their entire shopping journey: what a customer is looking at online, adding to their list, adding to their cart, purchasing both online and in store, how they rate each product after the purchase, and more. You can then use that information to form a conversation with each customer that is evolving, ongoing and unique to them.

The reality is that shoppers go back and forth between shopping online and in store. If you don’t connect this information you’ll end up marketing products that the shopper hasn’t purchased in ages because they’ve been doing their shopping in store recently. You need to look at behaviour in both scenarios, as well as pre and post purchase, so you can provide the most relevant 1-to-1 communications possible.

3. Ensure the experience is seamless across devices

Make sure that all your personalization efforts are connected across every consumer touchpoint so that your customer has a consistent shopping experience, however and whenever they shop. For example, if a user switches from desktop to mobile to in-store throughout their shopping journey with you, you are talking to them where they currently are, and you’re providing offers that are based on their actions regardless of where or when they happened.

In summary, you can create a personalized experience that is relevant, helpful and delightful by:

  • Using real-time information about the customer to speak to them at their exact moment of purchase decision
  • Using machine-learning algorithms to provide recommendations based on individual preferences and past purchases
  • Connecting data across pre-purchase, purchase and post purchase to create a continuous conversation with each customer
  • Connecting all consumer touchpoints to ensure you have 1-to-1 conversations that are consistent across all channels and devices

You’ll provide the best possible experience for your shopper and they will deeply appreciate that you’re not following them around the internet with ads for weeks at a time.

To learn more ways to increase shopper loyalty, download our whitepaper on Ten Guiding Principles for a Seamless Shopper Experience.

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