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

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

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

“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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