6 insights on data-based customer engagement

Although he used to work for Coca-Cola, Suat Alaybeyoglu doesn’t sugar coat the challenges of data-based customer engagement in today’s business environment.

Alaybeyoglu used to manage digital marketing technology for the soft drink giant in Europe. Today he’s the vice-president of consumer acquisition and management at American Express Canada. Most recently, he was one of the speakers at the Canadian Marketing Association’s analytics conference in Toronto.

Near the beginning of his presentation, he put up a diagram illustrating how — in a perfect world — data could be used to gather insights about customers, engage them, retain them and provide them with timely, relevant marketing and service options in an omni-channel universe.

“This is a nice dream,” Alaybeyoglu said, pointing to his tidy, idealized flow chart. “But it’s very difficult to achieve and much more painful to get there. There’s a difference between this beautiful vision and what you can actually achieve.”

Fortunately for those of us in attendance, he went on to pinpoint what some of those difficulties are and how organizations can tackle them. Here are the top six insights on data-based customer engagement from Alaybeyoglu and another conference presenter, Peter Danforth, senior director of loyalty and customer analytics at Loblaw Companies Ltd.

1. It’s tough and takes time.

Alaybeyoglu listed the biggest challenges to mapping out AMEX’s analytics strategy: customer channels that weren’t all digitally targetable, “walled gardens of data” and data that was sometimes “inconsistent, sparse and unstable” as well as difficult for advertisers to ingest and process. Topping it all off were privacy concerns, nothing to sneeze at in a regulated industry like finance.

It took AMEX four years and three special teams to create the analytics program it has today. So don’t expect to master your data demons overnight, he warned: “Organizational stamina is key to long-term success. It takes a lot of time and there are a lot of complexities.”

2. Get personal.

Using data just to target segments of your customer base doesn’t cut it anymore. It’s all about engaging each and every individual on a one-to-one level through personalization, Danforth said.

“Stop looking to segmentations to help define your customer strategies. No two customers are the same.”

3. Go all in.

Be fully committed to your analytics strategy from the top down.

“We really got senior leadership’s support behind us and then the company put real resources behind it,” said Alaybeyoglu.

4. Get it together.

Your data team has to be on the same page as your business units, Danforth said.

“Make sure there’s a direct line between the marketers and the people doing your advanced analytics work … If you don’t, then a data scientist is running your marketing program, you just don’t know it.”

5. Enable your data geeks.

Advanced analytics talent is not only scarcer than unicorns, it’s also expensive. Danforth said it’s even rarer to find a “hybrid talent,” someone who’s not only a data genius but also a great communicator. His advice? Instead of holding out to snag that elusive hybrid talent, hire the data genius and help them find the right words as needed.

“Enable an introverted data scientist who’s brilliant to communicate with you through someone else” who can fill in the communication gaps, he suggested.

6. Don’t aim for data perfection.

“Data can never be correct. It’s either sufficient or insufficient for a purpose. If you’re chasing ‘right,’ you’re going to be chasing forever,” Danforth cautioned.

A better approach, he said, is to look at whether your data ultimately serves the specific purposes you have in mind or helps your business answer its most important questions.

“Data is an enabler,” he said, “not an answer within itself.”

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