Don't be a dinosaur: data-based dispatches from the DAN Dialogue front lines

  • Marketing
  • December 5, 2012
  • Dennis Kibirev
 Don't be a dinosaur: data-based dispatches from the DAN Dialogue front lines
Mkyl Roventine via Flickr

The last DAN Dialogue breakfast event of the year recorded the largest turn-out ever, numbering about one-hundred and fifty in total—and for good reason. The line-up consisted of three senior marketing aficionados from BNZ, Farmers and Telecom, each of whom shared some interesting battle stories related to data wrangling in the digital age while bandying about esoteric terms such as propensity modeling, sequential patterns and other statistical trickery, none of which a modern marketer can seem to do without.

Who You Gonna Call?

Probably not who you were thinking... However, Justine Fairlie, customer and retention manager from BNZ, did mention ghosts when making reference to the almost non-existent marketing budget that was allocated for the purpose of figuring out who from BNZ’s massive customer base was in need of a “health check” call. The “health check” is carried out by the bank in the first year following a new customer signing up in order to ensure that everything is going well for them and to see if there is anything that can be done to improve the experience.

She set the record straight at the outset by pointing out that she’s not a “clever data geek so this is all ‘black box’ stuff …”, while acknowledging that “data geeks are invaluable”. So, data geek she is not but marketing magician she may well be, as her goal was to achieve a 20 percent growth target in FYE 2011 with the aforementioned ‘zero’ marketing budget. Needless to say, the goal was achieved in spite of this and numerous other obstacles: legacy processes and systems, no access to additional call centre resources in order to contact all target customers and a low likelihood of organic growth in the customer base.

In a nutshell, BNZ had to be pretty smart about which customers they called and ensure that both sales and marketing met their differing goals. They turned to customer data analytics for the solution, admitting that as far as sales and marketing go, ”data analysis and insights united us”. As Fairlie put it, “a desire to ‘do good’ by the customer is not enough, data provides the direction and a common language”. Working with their customer insights and business intelligence teams, they needed to figure out which of their customers had “unmet needs that could be fulfilled by BNZ”.

Customer and commercial benefits needed to be achieved simultaneously, which wasn’t easy given the hundreds of variables that had to be analysed for each customer, based on which the all-important decision of whether to give additional phone TLC was made. A complex statistical model had to be built and “high propensity” customers were prioritised when making outbound calls in order maximise ROI. As a result, the conversion rate improved by 15 percent (keeping sales happy), uplift increased by 40 percent (keeping marketing happy), and the bank now knows how to optimise their most “effective and expensive channel”, which makes everyone else happy. And all it really took was a little bit of “black box” data marketing magic.

Marketing is from Mars, IT is from Venus

That is what Grant Febery, direct & CRM marketing manager from Farmers Trading Company, discovered throughout the process of re-launching their loyalty card scheme. The Farmers Card has existed since the late '70s and, being a credit card, limitations existed with regards to its use as a marketing tool. The answer came in the form of the 'Farmers Club' scheme as a way of “rewarding and interacting with customers, and as a way of linking purchases to people”. As a result of the launch of this new loyalty scheme, the customer database sky-rocketed from 400,000 to 1,200,000 card holders in less than a couple of years. Not bad for a department store with a 102-year history, whose change process Febery euphemistically described as being, at times, similar to “moving a supertanker”. However, the key to success was taking “one bite at a time” in order to steer this supertanker in the right direction.

Of course, the end destination would have remained very far away indeed had it not been for heavy-duty analytics and engaging cross-functional teams, the same ingredients that were mentioned as being BNZ’s recipe for success. However, for Farmers, another ingredient was added to the mix: outside help in the form of various agencies that the company works with. “Data was the most interesting journey… where … getting expert help really hit home”. Like BNZ, there were a number of disparate legacy systems that had to be worked around in order to streamline the data into a single database for Farmers Club. The data Farmers migrated was “business critical” and it was crucial to ensure that they “got it right”, which is where their data mining agency played an important role.

“Wrestling with the data” is a very real challenge for Farmers, as it is for most marketers these days. Febery's solution may seem simple but has a certain simple truth to it: “You just have to get used to the feeling that you’re always behind in your analytics. Don’t worry, everyone else is too!” Using the metaphor that “data is the new oil”, he commented that, at launch, they were “oil rich but pipeline poor” and “the data was there, but it was spread across four different systems”, which means that they couldn’t get the information that was needed from a single source. Despite having a stable of business analysts, Farmers quickly realised that their analytics capabilities were lacking as they were “good at reporting and drowning in basic reports”, with the business not understanding the difference. The solution was to bring in Datamine, which worked with the Farmers in-house teams to form a hybrid client-agency model and add to their analytical capabilities.

“We’re in exciting times in terms of data. It is flowing and the business is interested in what it is saying”, and Farmers is “at the stage of deciphering behaviours and patterns and will do more insights-driven activities and life-cycle programmes, further reducing reliance on discounting.” The company will look to “develop increasingly complex contact strategies. Certainly, there will be more emails”, and, as for traditional media, it “will struggle to deliver the targeted messages and relevance that we want”. In conclusion, he mentioned that “…none of these options adequately solve our communications challenges, we’re going to be looking for new ways of communicating with customers and I’m sure that there are people out there in their garages, with an idea, finding new ways of talking to customers and we’ll be looking for those”. 

As for the age-old marketing and IT communications conundrum, he rightly pointed out that “we do talk different languages but we need to find a way to work together”. Your data analysis agency could just be the relationship counselor that you need. 

Avoiding Beautifully Automated Disasters

At Telecom, Chris Thompson is not only the marketing head for the company’s fixed-line business, but also the guy who’s flying the data-driven marketing flag within this behemoth organisation. In a company that has “masses and masses of data flowing through [the] systems, tons of it", this is not a job for the faint of heart. However, being a statistics geek and spending years doing linear programming and regression analysis in the forestry industry is apparently a way of preparing oneself for such a formidable undertaking.

“The possibilities of that are actually quite exciting”, and for a company whose relationship with its customer base “can be crudely defined as ‘twelve bills and a TV commercial’”, this may lead to some very interesting discoveries. “What we decided to do was go on a journey towards building a better way to serve our customers”. For Telecom, this may mean simple things such as reminding a customer to pay their bill. For other organisations, this may mean something seemingly minute but nevertheless ‘priceless’, as they say. He referred to it as “marketing so good, it feels like a service”, saying that “you can call it marketing, you can call it customer service but I say it is part of the relationship and part of maintaining the relationship, and driving lifetime value.”

As with the other speakers, he sees this “marketing-as-a-service” model being driven by analytics. For Telecom “the data challenge is the ‘biggie’” because there are so many systems, some of which still have ‘green screens’. Hence, they spent a couple of years and tens of millions of dollars in order to build a technology foundation, including tools that automate the marketing process. He sees automation as being indispensable for improving efficiency and effectiveness, and one that will allow Telecom to “… say the right thing to the right person at the right time and make some money”. The ‘proof of concept’ that was used to establish commercial viability for this data-driven approach made the company $1.2 million in three months. Not bad for a marketing campaign run by computers.

Another semi-futuristic way of using data to improve the marketing function involves pattern detection, which is something that is more commonly associated with the banking industry and, more specifically, picking up on credit card fraud. These same capabilities are now being used for optimising marketing campaigns, however Thompson mentioned the same problem of “over-subscription” exists that was mentioned by Febery earlier, which involves trying to figure out the right automated campaign that is optimal for the customer at a certain point in time. His answer was that “this is where a little bit of magic happens [and] it’s basically a giant linear programming exercise”, with the outcome being an answer to the question of which task will give you the best ROI based on various criteria that you, as a marketer, need to define.

Despite the emphasis on tools and systems, an important point Thompson made was that these are “only as good as your ability to use [them]”. “As a business owner, I go ‘it’s great that we have all of these tools, we got the Ferrari but do we know how to drive it and are we doing good with it?”. Ensuring that marketers don’t create “beautifully automated disasters” using the shiny new toys at their disposal is more of a cultural change, however, and he pointed out that they’re really striving to create a ‘learning culture’ at Telecom. This also involves being agile with regards to the way marketing and IT projects are approached and quickly learning from the outcomes, whether good or bad. 

After three years of being on 'The Journey', a key outtake of the process that he shared with the crowd sounds disarmingly simple: “data, data, data”, the crucial role of which he admitted to underestimating at the beginning. “People told me that it was difficult, I just didn’t realise just how difficult”. As part of the “maturity model” that was explained towards the end, he talked about striving towards being ‘world-class’ and said that “the future for us looks very different to the place where we started from”.

Don’t Be a Dinosaur

So there you have it: three different industries, three different speakers but similar challenges with respect to making sense of the new data-driven paradigm that is taking over the marketing world.And the key learning from these presentations? “Don’t be a dinosaur!”, as Chris bluntly put it. Because we all know what happened to them. 

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