Personalized messaging vs. personalized marketing

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How many marketing technology vendors say they help you personalize your messaging to your customers? Um, hundreds perhaps? Maybe all of them? The problem is, most of them do not personalize marketing.

The first gotcha is the analytics. Personalized messages and offers can work like magic to lift returns, but only if they truly interest the customer. Otherwise, to the customer, it’s just more noise.

So the analytics to determine what to say or what to offer each customer has got to be accurately “predictive.” That means something much more than demographic or behavioral segmentation. It’s sophisticated data science, beyond the scope of most marketing technology platforms not built specifically for this purpose.

The “ability” to personalize messages is not the same as delivering messages that motivate. Messaging is not always marketing.

The next gotcha is “personalized selling.” Most platforms that personalize communications try to build up a profile of behaviors and attributes that may indicate preferences and interests. When the presence of those behaviors and attributes are detected, appropriate messages and offers can be delivered in real-time.

This is where the action is in marketing technology today, with a wide range of complexity and effectiveness available. There’s a lot of data to gather, integrate and interpret, and a lot of rules and actions to configure, but it can enhance the experience of a website visitor or encourage a customer to buy, if and when the right behaviors are detected.

This is all fine and good, but it’s personalized selling. Personalized selling is messaging to one customer at a time in a given context. It’s not marketing. Marketing is messaging to a market.

So the last gotcha is marketing at campaign scale. If you are going to do marketing — that is, message to a market of say thousands or millions of customers at once to stimulate demand — and you want to personalize it, then you think in terms of campaigns.

If you have a lot of potential buyers to reach and hundreds or many thousands of products to offer them, how do you identify exactly the right product (or set of products) to promote to each individual customer in your campaign the exact products they are most likely to buy? It’s an intractable problem for almost every marketing platform.

This is why most marketers campaign around specific products — the one or few they want to promote in a given campaign — and have someone do all the analytic modeling needed to find the best customers for those specific few products. Finding the best customers for every product, or any product on a moment’s notice, or the best product(s) for every individual customer is cost-prohibitive — never mind incompatible with campaign deadlines.

However, there are some new approaches emerging that enable marketers to cost-effectively and easily execute personalized marketing campaigns rapidly and at scale. The secret has to do with one final gotcha inherent in most marketing technologies that personalize messages.

They lose site of the ultimate objective of marketing: getting people to buy your products and services. Instead of directly answering the questions — what is each customer most likely to buy and when — the analytics are aligned toward predicting answers to all sorts of other, often important but not as important, questions. Questions such as:

  • What communication channels do customers prefer?
  • Who is a good brand advocate, and whose sentiment is turning unfavorable?
  • Who seems to be sending buying signals?
  • What kinds of content do they seem to like?

The secret is staying focused on predicting the main objective (getting people to buy your products or services), reducing the data complexity by employing more advanced analytics that can get the most out of only the most necessary data, and automating the entire analytic process for scale.

This approach is making personalized marketing truly practical for the first time.

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