Anticipating Your Customers’ Needs, Amazon (and Smart Customers) Style


Share on LinkedIn

Either Mr. Bezos and his team of strategists read our book (did you get the copy we sent you, Jeff?) and/or Bruce Kasanoff and I were looking around the same corners Amazon did when we wrote these words a few years ago: Anticipate the needs of your customers by understanding the data that increasingly surrounds them.

Late last year, Amazon was granted a patent for what it calls a “system for anticipatory package shipping,” which basically means they plan to start delivering packages before customers even think about clicking “buy.” Put another way, they’re planning to ship the things they know their customers want, before their customers even know they want them.

While this might sound like science fiction, most companies have access to massive data about their customers, giving them the potential ability to better anticipate customer needs. But they don’t. The truth is, most companies don’t even make an effort to anticipate what their customers will do – or need – next, much less respond to them.

Which is why the ability to use customer data to anticipate customer needs (and intelligently respond as a result) is what will increasingly separate companies that thrive from those that have no future at all.  Like Amazon, leveraging customer data can help your company become dramatically more intelligent, and more responsive in your customer’s eyes.


Only “intelligent” companies will thrive

Bruce and I wrote Smart Customers, Stupid Companies based on the premise that technology-driven innovation is forever changing customer expectations of experience with the companies that wish to serve them. What’s happening today – as with nearly all disruptive innovation – is an amazing opportunity for established companies to reinvent themselves and disrupt their own industries.

At the same time, these disruptive forces are making it impossible for firms to survive with outdated strategies, as the first signs of increasing marginalization emerge. Those that react slowly or tentatively to these signs are the next Kodak, Polaroid, Blockbuster or Borders – and they won’t recognize what’s happening until it’s too late.

The fact is, only the most “intelligent” companies will be able to respond to – and profit from – these radically greater customer expectations. That’s because the same disruptive forces driving these changes make it possible to radically improve customer experiences. And one way to do so is to anticipate customer needs.

Understanding your data can help you anticipate customer needs.

Whether you have 1,000, 10,000 or 10 million customers, every interaction they have with you leaves behind a trail of potentially valuable data. This data isn’t limited to that controlled by you, or generated by interactions with you. Among other things, it can include opinion, commentary, and other unstructured and unsolicited data available across the social Web.

Even a few years ago, the resources required to store and analyze the massive volumes of data generated by your customers meant that large, established companies held the advantage. No more. In fact, the legacy systems that gather this data and store it in disconnected operational silos means the advantage has shifted to smaller or more nimble organizations, often better equipped to leverage things like cloud-based data storage and BI (business intelligence) tools.

With the ability to analyze and generate insights from massive amounts of complex customer data well within the reach of any company, there’s no excuse not to be smarter about the ways this data can inform strategy and decisions. Even so, many established firms simply have not leveraged technology or shifted their organizational structures, business processes, products or distribution channels in ways that permit intelligent use of customer insights.

Soon, it will be too late for them. Because smart customers simply expect more – and smart companies are increasingly giving it to them.

Pattern recognition is what intelligence is all about.

Simply look at the data surrounding your customers – how and when they look for help, what they buy and how they use your services, for example – and patterns and insights will quickly emerge. You can, for example, identify patterns in website usage, customer service calls, customer orders and product returns. By doing so, you can begin to actively predict what is going to happen next, so you can anticipate what a customer is going to need at each step of the customer journey.

To this, what Amazon is patenting is the monitoring of customer buying patterns and purchases, and applying that part of a customer’s history to other people in the area with similar interests. The other part of their patent is the actual logistics of getting those packages to shipping centers and on trucks, to get them closer to these customers.

You don’t have to have the distribution and logistical prowess of an Amazon to drive benefits for you and your customers. By bringing the discipline of customer data analysis to your business, you can use data-driven insights to anticipate customer service needs, or even prevent annoying incidents from happening at all.  And you probably have the data to start identifying these patterns already.

The implications are significant. What if you knew something was going to happen a day, week, or month from now? Or if you know your customer tried to find an answer but was unsuccessful? You can do something as simple as calling that customer, rather than making them reach out to you (or a competitor).

The truth is, intelligently interacting with your customers is the best way to get more business from them. It’s also one of the best ways to differentiate from your competition – after all, only you have access to the data surrounding your customers.

Like Amazon and anticipatory shipping, being in the unique position of knowing exactlywhat your customers want and need opens up all kinds of intriguing possibilities…

Republished with author's permission from original post.


Please use comments to add value to the discussion. Maximum one link to an educational blog post or article. We will NOT PUBLISH brief comments like "good post," comments that mainly promote links, or comments with links to companies, products, or services.

Please enter your comment!
Please enter your name here