If you get your Big Data priorities right, say two agency experts in the field, crucial consumer behaviours can be understood.
Using Big Data successfully in advertising is all about managing complexity. As constraints on data tracking and the costs of storage are reduced, talking about what to do with data becomes more important than talking about how to get it.
“The most important thing to know about big data is that it is an input and a toolkit, not a solution,” says James Bibby, Director of Strategic Technology at Cossette in Toronto. “A large part of the conversation has been focussed on how to collect data and the big data tools themselves rather than what all this data is actually for. What I think we should be talking about is how to weave curiosity and a culture of experimentation through the fabric of a company.”
Setting Priorities
Prioritizing the information search and identifying what is useful is the key to preventing information overload.
“Data, information, knowledge is available to us and to the brands we support every second of every day,” says Matt Orlando, the CEO of TAG in Toronto. “Accessing, analyzing, and acting upon all of it, in real time is overwhelming and, often times, an impossibility. We tend to focus on trends that data allows to see as they happen.”
He adds: “Looking for consistencies and commonalities allows us to make informed decisions to support long-term brand health including changing course where required. Short-term, however, it’s important to prioritize. Look for the top 3 data points and mobilize to react to them versus trying to hit them all. A series of small wins in succession amounts to more than simply talking about a long list of them.”
Cultures of Collaboration
Big Data belongs to and is collected by both agencies and their clients. Tracking the success of a campaign requires clients and their agencies to work together.
“Understanding how consumers buy and their decision making process is crucial. So collaboration with client data, coupled with tracked campaign intelligence can fuel more predictive marketing programming,” says Orlando. “But again, it has to be quick and it has to be in small doses or by the time you sit and analyze and discuss and plan, it may simply be too late.”
Insightful analytics come from using the appropriate tools and the appropriate methods that have to be defined early on.
In many cases, clients can benefit from clear analytics and optimization techniques such as A/B and Multivariate testing. These techniques can yield amazing insights into customers and allow us to evaluate our hypothesis on anything from copy to design to functionality, says Bibby at Cossette.
“One great example is the use of big data and econometric models to examine the effectiveness of traditional and digital advertising on offline transactions. Without big data and some fairly sophisticated mathematical models it is very difficult to unravel the many confounding variables. The question we try to answer going into any project like this, is whether we have exhausted the utility of the data we have before recommending or embarking on a big data initiative.”
What Does the B2B Enterprise Need to Master Big Data?
B2B enterprises need to build the basic tool-kit and basic expertise to deal with Big Data.
“Clients need to invest in the tools to acquire integrated data, to employ the people that can distill it down so that everyone gets it and finally to partner with agencies that are big on nimble and small on ego,” says Orlando at TAG.
The first item in that Big Data basic plan, though, may be to ensure you’ve used your basic analytics to its full advantage.
“Many companies don’t need to worry about building an expensive big data practice,” says Bibby. “Until they have exhausted the basic data and analytics they have, it is unlikely they need to start collecting data at the scale where we would start to call it Big. What companies, and indeed agencies, need before they embark on building a big data practice, is to come up with interesting and difficult questions.”
