How to truly digitalise your digital marketing

Most marketing efforts these days, digital marketing efforts that is, are based on multichannel strategies, applying social media strategies to traditional marketing problems in a more or less planned fashion. What many companies miss so far is however to adapt their data mining efforts from pure web analytics to modern day digital intelligence frameworks.

As shown by Forrester Research, the evolution of digital intelligence has surpassed data mining efforts and moved on to close the gap between today’s multiple customer touch-points and the company’s intelligence frameworks.

Evolution of Digital Intelligence

Evolution of Digital Intelligence

Why haven’t companies moved on or benefited from Digital Intelligence frameworks?

For starters, digital intelligence surpasses web marketing efforts not only by complexity but also by the lack of reporting simplicity based on the sheer amount of data at one’s hand. Many marketing departments and agencies do lack the manpower to deal with this new flow of data in both quantity and quality.

The paradox of choice comes in second. Digital data is almost infinite and real time. A day however remains at 24 hours and the human capacity to deal with data complexity hasn’t changed either. Thus selecting data sets of importance is still an issue many marketers face to create meaningful data reports to drive – and that is the most important part – change!

Silo thinking prevails! Even if marketing has moved on and implemented the most efficient, tactical and real time digital intelligence framework ever seen by mankind, does that mean sales, R&D and the rest of the organisation’s top management are likely to change directions based on marketing’s new stream of data reportings? Unlikely!

The 6 steps to implementing a functioning Digital Intelligence framework:

1) Understand your companies business model and strategy! Data mining, intelligence and reports are great but only if they track and measure the right metrics to help intelligent management decision making to drive the company forward to reaching its set goals.

2) Define a set of relevant KPI’s! KPI’s are indicator’s for what has been defined as a state of success of advancement. If you end up with a list of 50 relevant KPI’s – think it over and start again. For many organisations anything from 5-10 is most likely enough at this level.

3) Derive measures to track your progress! Divide these measures into lead and lag measures to not shift from pure historical data mining effort to a framework to enable educated management decision making. E.g it is great to know that in the last quarter 7% of your customers have increased their mobile spendings but it might be to late to prepare your backend to handle the change in customer spent! The more meaningful lead measures, the more enjoyable lag reporting you will have.

4) Get other managers on board! This could well be step number 1 or even step 0 before you start any effort. Knowing key decision makers and influencers in your organisation will help to drive a high key implementation of a customer centric digital intelligence system. Product development needs to be as much on board as your sales teams, in-house support and other departments.

5) Built meaningful reporting and not an all in one dashboard! With all the world’s data at your hands, select data sets that are relevant to key decision making processes. This data will also help to get others on board and built up use cases. Less is more!

6) Revise your data handling efforts! Assure your data sets are flawless, protected and up to all legal data handling standards within your area of operation. The last thing you want is a disgruntled intern to blog your new most valuable assets for a few clicks, likes and big open eyes of your competition.






Tagged , , , ,

Hinterlasse eine Antwort

Deine E-Mail-Adresse wird nicht veröffentlicht. Erforderliche Felder sind markiert *


Du kannst folgende HTML-Tags benutzen: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <strike> <strong>