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How to turn big data into great customer experiences

How to turn big data into great customer experiences Paul Dunay

The phenomenon of big data certainly comes with big promise. After all, having terabytes of data on customer history and behavior is certainly better than trying to extrapolate from just a few data points.

For sure, online marketers who make sense of big data will be able to build customer experiences around hard data and evidence, rather than on hunches and guesswork. Instead of working on intuition, or crude analytics, you could use definitive evidence to design product pages that lure your best customers directly toward the shopping cart. You'd know exactly when to introduce your promotions and offers, and you'd know which promotion would work best with each particular customer. You could optimize your online interface, so that everything from search to registration to "Place Order" was virtually friction-free.

Getting to that point, however, requires first harnessing the data. It is no small feat to integrate huge amounts of data from a variety of sources. It is even trickier to figure out exactly how to translate that information into more visits and fuller shopping carts -- in real-time, customer by customer.

This is why very few marketers are making maximum use of the data they already have.

The good news is, there are technologies and tools that make it much easier to find the gold hidden in terabytes of data -- and use it to refine your online marketing with laser precision. But there's a mindset at work here, too -- a way of thinking about data that may involve some shifts in culture, depending on where your organization is right now.

Having worked with a number of online marketers who needed to tame big data, here are six steps that will help you get there:

Think continuous evolution and iteration, not instantaneous

Yes, big data can fundamentally shift the way you do business. But don't try to change everything at once.

It's far more productive to adopt a "test-and-learn" philosophy. Two dozen incremental improvements in site design or wording or personalization will get you much further than trying to "innovate" in one fell swoop. We see this every day.

The most successful marketers are optimizing and refining all the time. They steadily move ahead, with a thousand baby steps, finding something to improve almost every day.

Note: This may call for some adjustments to web development processes. The most agile marketers can typically go "live" with tweaks, adjustments, or tests in a matter of hours. (Slow marketers wait for the next release. Don't do that.)

Align big data goals with your individual business goals

Create separate initiatives or projects for each of your business goals, such as acquiring new customers, boosting conversion rates, improving customer loyalty, or increasing lifetime customer value. This makes it much easier to determine what type of data to reel in, and exactly how to use it. Focus a team or a project on one objective at a time.

Sell the concept internally

In some organizations, moving toward data-driven, evidence-based marketing may call for some extra communication to get everyone on board:

  • Encourage knowledge sharing and continual learning. Let everyone know what you found out.

  • Simplify everything. Present data and outcomes in easy-to-understand terms that managers can use to make decisions (pictures, graphs, etc.)

  • Communicate plans and achievements across the organization. Don't hide results.

Create one team for big data

You will need to include marketing strategists, analytics gurus, and web developers -- and especially creatives, who may sometimes feel threatened or hampered by having to work with hard evidence.

Then integrate with those responsible for e-commerce and site optimization. No silos allowed.

Find a committed, obsessed, dedicated executive to drive the process and act as a focus for future customer experience innovations.

Your own data is best -- by far

The real-time data that your website and CRM systems are gathering is far more valuable than anything you can obtain from an outside vendor. Because it's about your own living, breathing customers.

And it is data that your competitors don't have. Advantage, you.

Here are examples of the typical aggregate data you can capitalize on in a big data strategy:

  • Acquisition source

  • Geography

  • Interaction behavior

  • Transaction behavior

  • Recency of visits

  • Frequency of visits

  • Social attributes

  • Form inputs

  • Conversion rates

  • Conversion values by product or category interests

  • Channel/Device

Aim for real-time optimization, customer by customer

For most marketers, the goal should be to make in-session decisions as to what customers should see, what offers you recommend, and what you say to them.

Craft a custom experience for each visitor, and they'll buy more.

Do all of this, and they'll be back.

Paul Dunay is the global vice president of marketing for Maxymiser.

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"Big data exabyte terrabyte or gigabyte" image via Shutterstock.

Paul Dunay is an award-winning B2B marketing expert with more than 20 years’ success in generating demand and creating buzz for leading technology, consumer products, financial services and professional services organizations. Paul is the...

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to leave comments.

Commenter: Shep Hyken

2013, May 05

Sometimes Big Data translates to "Too Much Data.” The key is to break down the data into usable information. The hard part is to determine what is usable. Sometimes the data gives us so much information that it causes "analysis paralysis.” Big Data works best when it morphs into "Simple Data.”

Commenter: Paul Dunay

2013, April 30

@Jack - you are right and it brings up a good point to identify the metrics you think are important so you can map out what data those metrics require!

Commenter: Jack Gazdik

2013, April 29

Great read Paul. I couldn't agree more with what you had to say about aligning your big data goals with your individual business goals. Just because you have access to an incredible amount of data doesn't mean all of it is relevant to your campaign and its goals. It's important to remember that data analyzation should start before you actually have any data, by determining which metrics are important to track and which metrics may just be a waste of time.