How do you connect with your customers when you don’t know
who they are? This is the situation every business-to-business
(B2B) company deals with. When I was working with Cisco to improve their
software licensing process, one of their key challenges was knowing exactly who
purchased their software. That’s because most of Cisco’s business is done
through indirect channels (e.g., value added resellers). It’s a fine business
model that’s obviously worked out well for B2B companies like Cisco; however,
it does present a challenge when you need feedback from your end users.
Fortunately for B2Bs, big data analytics opens up great opportunities for
connecting with end users, if they take a strategic approach.

Why Marketing Needs Big Data
Analytics

The marketing organization in a B2B has a formidable
challenge when it comes to direct customer feedback. As a consultant that
specializes in helping executives increase their marketing effectiveness, I’m
very close to the challenges they face in justifying their budgets. To build
their case, marketing executives must have reliable data on the effectiveness
of their marketing campaigns; however, some things are difficult to measure
(e.g, brand awareness). This challenge is exacerbated when the majority of your
business goes to channel partners who closely guard their relationship with
their customers – who happen to be your end users.

One way B2B companies can overcome this challenge is by
building a big data analytic product or service. For instance, imagine a
company like Duck
Commander
, the one featured on the hit show Duck Dynasty, who makes duck
calls. Now imagine this company sells the vast majority of its duck calls
through channel partners. So they don’t have much end user data in their
databases right now. One strategy is to develop an analytic service that tracks
where the best duck hunting is by geography, exclusively for people who buy
their duck calls.

End users would sign up for this service at the company
website, providing basic personal information and the serial number of their
duck call. If the company charged for this service and used it as a profit
center, this would be considered a product strategy. If the company offered
this service for free, it would be considered a relationship strategy. Either
way, marketing would now have valuable end user information that it never had.

Your mission, if you choose to accept
it

 To put this strategy in motion, the very first thing you
must clarify is whether to pursue a product strategy or a relationship
strategy.

If you pursue this as a product strategy, then the goal is
to make money – period. The success of this strategy is measured by its ability
to generate profits for the company, just like any other product or service.
You should first approach your data science team to find out if this idea is
marketable.

A good practice is to involve your channel partners in this
exercise. Not only do they have your end user data, but maintaining your
relationship with them is of paramount importance. The benefit for your channel
partners is that, although this analysis is valuable for them, they probably
don’t have the resources to do it. In this regard, it’s a win-win for both of you.
After you have some statistical evidence that your idea is marketable, engage
your data science team to develop the analytics that will drive your new
offering.

If however your decision is to pursue this as a relationship
strategy, you must take a completely different attitude on the approach. This
analytic service will be a free service – period. It will never make money, and
it’s not intended to make money. So, you need a supporting business case to
come from somewhere. The natural tendency is to build it against marketing – after
all, they’re the ones who benefit the most, right? That’s true, but it’s not
fair. Marketing has a hard enough time justifying its budget, so charging
Marketing for the development and maintenance of your new analytic service puts
them in an unfair position.

The business case must be attached to an existing product – duck
calls in our example. That means the duck calls line of business must generate
enough revenue to support both the manufacturing of the calls and the analytic
service that’s attached to it, and still provide the company with a reasonable
profit. If things go right, the end user loyalty engendered by this
“free” analytic service justifies the cost of developing and
maintaining it.

In this case, you probably don’t need the data scientists
involved in building the business case; the financial analysts can do this.
Once the business case is in place, have the data science team go straight to
work on the analytic service. Again, don’t exclude your channel partners;
they’re an important part of this whole strategy.

Bottom line

Trying to connect with end users is a classic problem for B2B
companies – one that a big data strategy can overcome. Having your data science
team build an analytic product is a great way to collect much needed end user
information. However, you must be clear on what strategy you’re pursuing. Are
you building a product or are you building a relationship? Either way, your
success will put Marketing in a much better position to analyze their
effectiveness.

Take some time today to talk with Marketing about how they
might be able to use this information. Then if it makes sense, start getting
your data science team ready for their next big adventure. Who knows, it could
be the start of your new dynasty.