A McKinsey analysis of more than 250 engagements over five years has revealed that companies that put data at the center of the marketing and sales decisions improve their marketing return on investment (MROI) by 15 – 20 percent. That adds up to $150 – $200 billion of additional value based on global annual marketing spend of an estimated $1 trillion
So, the fact that Big Data leverage can help organizations get more returns on investment is a no-brainer. Just a small statistic again so that we fathom the volume of data that one needs to navigate-
- Facebook ingests approximately 500 times more data each day than the New York Stock Exchange (NYSE). Twitter is storing at least 12 times more data each day than the NYSE.
One can comprehend the challenge that an organization faces in terms of harnessing big data and deploying the right analytics approach to decipher the same.
And that is the reason an IBM survey shows that:
- Seventy-one percent of chief marketing officers around the globe feel their organization is unprepared to deal with the explosion of big data over the next few years.
A Few Tips To Use Big Data in Marketing
Redefine and focus on Nano goals- The marketing and the organization at large needs to deviate from 2 or 3 macro goals-like maximize profits or increase sales- and instead create multiple nano goals. So there can be a set of 10 nano goas against 1 macro goal.
The reason for doing this is that:
- One gets smaller streams and volumes of data against each nano-goals.
- You will be now able to connect unstructured and spontaneous data in real time across the goal flow/process.
- Make micro goals or even better , divide your broad goal into various micro questions that need to be answered and then attach each question with pieces of real time data- that, perhaps will throw some meaning
Capture Intent- The one thing that brands need to focus is on ‘intent’ and they need to measure intent via various actions. For example for an ecommerce marketplace platform, various actions from discovery to buying can be broken down into intent-actions, like clicking on price, number of pages viewed in a single category in a single session etc.
Profile- Based on intent capture and integrating with location and demographic data (if available) one can create profiles of customers and can repeatedly observe to derive a pattern or behavior
Connecting, attributing & Optimizing: These profiles can then be shown real time advertising or product suggestion from the huge pool of marketplace data. The data can reveal interesting analysis like what % of budgets TV takes up against YouTube and in turn drives what % of organic search traffic or category page-views. So with big data coupled with advanced analytics, one can attribute a result on web platforms for example through action taken on TV or search or social media. One can hence also visualize what happens to overall search traffic or page-views or purchases if there is a 15% cut in TV advertising. And then if you do the attribution and optimization over a period of time one can get a model defining the perfect spend ratios for each advertising vehicle.
In one of the upcoming articles we will take this discussion forward by adding other manifestations, in the meanwhile please feel free to send in your articles or suggestions to email@example.com