Kiss: keep it simple, stupid!
Let me see if I understand Big Data enough to explain it simply
The suite of IT products that enable Big Data is extensive and growing
Big Data is likely very overhyped, as reported by Garnter
As the DMA reminds us, Big Data is NOT a strategy
Nor does it provide competitive advantage. Big data is a tool, nothing more, nothing less
Again from the DMA, more data doesn’t necessarily mean more insight, sometimes more is less
And the old rules still apply: such as garbage in, garbage out, e.g. just having more data doesn’t help unless it is the right data
As we learn from Harvard Business Review where they talk to the focusing on getting the right data, not lots of data
And then there is the pacman myth, where people believe that all you need is “pacman IT’ to gobble up data and spit out the answers. It just doesn’t work that way.
There is a lot of overpromise – most companies would report mainly investments to date, little if any profit improvement and certainly not 10%
And when big data is employed, it doesn’t necessarily make a difference. In this case, despite winning a $1 million prize, the big data solution was never implemented.
The promise of big consumer data, personalized marketing, is still largely more a promise than a reality. As seen here, four square thinks that bus riders need to buy gas
And Smarter Travel is still trying to sell me a ticket to Barcelona, 6 months after they know I went there.
Solution One: we need to close the gap between end users and senior management, who have business problems where big data could help and the data scientists, who with their tools, could help. But today, the don’t communicate because they speak different languages
What I believe is needed is a new role, the Big Data Translator or Integrator, who understands both languages and can insure that information needs are translated into appropriate analysis and the analysis output is retranslated into information that the end users and senior managers can use
Marketers want both efficiency (reach more of the right people) and effectiveness (more responses from those they do reach)
Consumers respond when messages are perceived as service (not sales) and when they are engaged (not annoyed)
The consumer data mall seeks to deliver both via Dialogue (versus monologue, with permission, not passivity, seeking a win win value exchange
How does this work
Marketers send data to the “mall”, which combines data from various sources. They also send messages and offers they would like to use
The Mall sends back a “score” that predicts how likely a person is to respond to an offer
The scores are used to decide who gets an offer. Importantly, marketers agree NOT to send to those with low scores, as this is unlikely to lead to a response and it annoys those customers (or leads them to ignore messages in general)
Consumers feedback both via their responses and by providing responses to information requests
Up to 4 Anchor tenants who bring a strong core of customers and customer data: Telco, Transaction processor and a food and hard goods retailers
Category exclusivity, so they can benefit by building share
Additional tenants who bring more data, e.g. airlines, hotels, restaurants, insurance, automotive, medical
Non-exclusive, they benefit by using data (via marketing scores)
Kiosks, e.g. local merchants, dry cleaner, restaurants, they mostly use data, but contribute little additional data
Consumers, they opt in to participating and share additional data
In return they get relevant messages. So engagement increases by a step function, as they welcome, rather than ignore messages and offers.
The challenge for marketers is to increase the relevance of the messaging – sending the same “mass market message” to either everyone or selected segments ignores the need to customize the message, not just the target.
Initially, they may get point, miles, etc as an incentive to join, especially if one of the anchors has an existing program or an airline joins (in which case they might get category exclusivity). But the longer term value proposition is “less spam, more communications you like”.
A key tenet is that the reinforcement between sharing data and getting something relevant back should be very quick – the mall should seek to expliciity reinforce the value of sharing data.