Tag Archives: customer

How big data is remaking customer loyalty programs

Retailers spend about $2 billion every year to build and run loyalty card programs in the hopes of creating lifelong, devoted customers. However, those loyalty programs often fail to deliver as advertised. But now, advanced analytic techniques running on big data platforms like Hadoop promise to help retailers get closer than ever to realizing their “one-to-one” marketing dreams.

Part of the problem with traditional loyalty programs is the lack of good, clean data. When people sign up for programs, they often refuse to answer questions in the questionnaire, or they intentionally lie about their age, marital status, or whether they have kids. In many cases, all they care about is getting the 5 percent “Club Price” on broccoli at Von’s or getting the 12th tall coffee for free at Starbucks. They could care less about whether the retail has accurate data.

All that bad information meant companies often scale back on their plans of running highly targeted marketing campaigns, says Andrew Robbins, the CEO and founder of Paytronix, which helps companies execute customer loyalty programs.

“There are things every marketer in the world knows: You should segment your guest base, you should think of targeted rewards, and you should run targeted campaigns,” Robbins says. “We were finding customers weren’t doing that. They were just blasting everyone.”

Marketers had grown sour on loyalty programs due to a “relevancy gap,” Robbins says. Instead of sending offers to specific customer segments, the lack of trust in the makeup of those segments was leading to a shotgun approach. While a single man in his mid-30s may appreciate an offer for $5 off a kid’s meal composed of a grilled cheese sandwich and an 8-ounce apple juice, a more effective offer, research has shown, may involve a half-pound cheeseburger and a 16-ounce beer.

Age is a critical factor in marketing, but it turns out that people lie about how old they are. “About 10 percent of people lie, and another 20 to 25 percent won’t answer,” Robbins says. Getting information about children in the household is also tricky. “There are lots of moms who don’t want to tell you they have kids because they’re afraid for their kids’ safety. We ask these questions 50 different ways and all of them generate pretty bad data.”

That’s where big data comes in. Instead of taking the direct approach and asking people todescribe themselves, the modern marketer can use external sources of data and advanced analytics to infer things about her customers. Instead of asking customers to describe themselves, one can accurately ascertain facts just by observing their behavior. For example, if somebody buys a cheese pizza and a milk, “it’s much more likely to be a substitute for a kids meal than it is for an adult,” Robbins says. Similarly, mining for likes on Facebook and Twitter can reveal very detailed preference data for individuals.

Using this approach, a marketer can segment their customer base with 95 percent accuracy, Robbins says. The downside of this approach is that it requires more data. In fact, it requires about 1,000 times more data than the old approach, according to Robbins. That’s why Paytronix decided to abandon SQL Server as a data warehousing platform and invest in Hadoop.

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Big Data Validation

Today, Paytronix runs Cloudera‘s Distribution of Hadoop (CDH) on Amazon’s cloud service. SQL Server still has a role in serving insights directly to Paytronix’s customers, which includes companies like Panera Breads and Outback Steakhouse. But for advanced analytics, the relational data store is no more.

Before Hadoop, Paytronix only stored the demographic and loyalty data. But with CDH, a big data application from Platfora, the power of R, and BI tools from Pentaho, a relatively small groups of data engineers at Paytronix has the tools to dive inside the fine-grained data and pull out relevant patterns.

The bulk of the additional data is contained in the “checks,” or the customer receipts generated by each restaurant transaction pulled from the point of sale (POS) system. That’s the gold that Paytronix was after. But keeping track of all that data is no easy task, and requires powerful tools for validating and mixing the data.

“You want to make sure that each field within a check makes sense: How they paid the cashier, the table they sat at, the memo information that’s just typed into the check that says ‘Salad dressing on side–peanut allergy,’” Robbins says. “A lot of this information might be just typed into check in free-form fields.”

Being “close enough” is not good enough in this line of work, so Paytronix takes steps to ensure the data is accurate before a customer acts upon it. “When you have thousands and thousands of these stores all throwing data in, their data could look good. It could be 90 percent correct, but portions of it could be horrible,” says Robbins, a veteran in this field who has degrees from Princeton, MIT, and Harvard. “That’s a data validation problem, and if you don’t try to fix that before you mix it with something else,” you’re asking for trouble.

In the old days, Paytronix would have used ETL tools to build multi- dimensional cubes to validate the data before acting upon it. But that was a slow and time-consuming process. Instead, the company now speeds up the process with Platfora. “They have this really elegant tool that lets you point at raw data in Hadoop, define a cube in an abstract language they call it Lens, and then visualize it. That can be done by a business user, not a software engineer,” Robbins says.

Data in the Mix

“For most retailers, to get to one to one, you’re probably talking about 100 to 1,000 segments, overlaid with personalized communication,” Robbins says. “Maybe the strategy would be, for this segment, I’m going to give them a discount on the last item they bought. In the end, it’s a one to one strategy.”

This is where getting the small things right–like the peanut allergy, the preference for soy milk in coffee, or the preference for hand-tossed pizza–counts a lot. No one person or team of people can be expected to track all this data manually. But thanks to big data tools and technologies, companies can act on this data, and do so with confidence. For marketers looking to build a customer loyalty program, that’s a potential game-winner that can’t be ignored.

Source :datanami.com/2014/12/08/big-data-remaking-customer-loyalty-programs

Mid-market turns attention to customer loyalty

The top strategies for mid-market businesses over the next twelve months or so are increasing customer loyalty and reducing operating costs, according to a survey of over 2,000 decision-makers in mid-market businesses, conducted by research firm Populus.

More than one third of respondents saw increased customer loyalty as the means by which they would grow their business over the next year. The study interviewed decision makers in businesses with 100 or more employees as part of its annual Business Index Survey, which gathers insights across 18 countries around the world.

“As confidence for mid-market companies reaches new highs, businesses are planning for growth by focusing on increased customer loyalty,” explained Jayne Archbold, CEO for Sage Mid-Market Europe. “We also found that Europe’s mid-market companies are pinning their hopes for growth on strengthening their product and services portfolios and marketing.”

Customer loyalty
By focusing on customer loyalty, businesses are demonstrating realism and pragmatism. They have understood the huge value that happy customers bring. Gaining new customers is expensive and time consuming, and customer churn means that is multiplied many times over. Dissatisfied customers can also spread the news of their unhappiness far and wide and a negative sentiment can have a huge effect on profitability.

A content customer is your advocate: the happiest will go out of their way to sing your praises, recruit new customers and provide constructive feedback on your products and services. There are knock-on effects too. Employees who feel they’re doing something worthwhile, and who work with satisfied, positive customers, tend to stick around longer – and provide better service, because they’re happier doing their job, too.

Customer loyalty really comes into its own when a business is in a growth market. Customers spend more – making them more valuable and helping to boost growth organically. It also requires less outlay than recruiting new customers.

Being customer-centric
There are many reasons why a culture of customer centricity makes even more sense these days. The emergence of the social customer – who can react to a bad experience on social media with catastrophic repercussions – is one reason why customer satisfaction has become a mission-critical issue for many businesses.

Driven by technology opportunities people want to communicate and collaborate more in business, as they do in their personal lives. Gartner predicts that by 2016, more than 1.5 billion people will use social networks. There is a huge opportunity here for customer loyalty.

Customers are interacting with brands and businesses, creating deep attachments, and communicating more often. This gives connected companies more insight, enabling them to create yet richer interactions and better communications, products and services. Beginners on this journey will find that by broadening their presence on social media they create an extra avenue to generate interest. If people can find the business in multiple places they are more likely to make that connection between the brand and their need when they are ready to buy.

Then the customer service team can use the increased visibility into the customers and make every agent more productive, empowering them to upsell and cross-sell.

Customer centricity is not just about offering great service, it means offering a great experience all the way through the customer journey, from initial awareness through purchasing and finally the post-purchase process. Companies that are committed to customer centricity focus on what the customer wants and needs, and develop products and services around that.

Communication and collaboration is quicker, easier, and far more natural than it ever was before the advent of modern collaborative tools. It’s not just for customers though. When staff are socially connected they also become more engaged and more productive.

Source : www.thewisemarketer.com/news