Tag Archives: DDL Insight

Big Data or Smart Data?

The hot topic at any marketing, insight and data analytics conferences at the present time is Big Data. In our increasingly technological driven world we are generating enormous volumes of data, some estimates suggest the world is adding several quintillion (1018) bytes of data each day. And as marketeers and analysts we see this as a goldmine of information and we are instinctively driven to find ways of making use of it.

In terms of marketing activity, “Big Data” relates to the ever growing data sets created by our customers and prospects, not just through the traditional marketing database or CRM, but also from retail and eCommerce activity, from social media sites such as Facebook, Twitter, LinkedIn, Flickr and Foursquare. We can also access data from sources such as Google Analytics where we can see how visitors find our websites and what they then do once there, and media can include the full range of digital formats such as pictures, video,audio, CCTV.

For example a financial lender may wish to  look at an applicants Facebook site as part of the credit checking processes to see if they have pictures of holidays every other month and parties every night – would they be a safe bet to lend to?  We have all heard stories of employees checking out social media sites of potential employees as to their suitability.


Would you give a pay-day loan to someone boasting this shoe wardrobe on Facebook?

The immediate consequence of wanting big data is that you need big servers and big software to process it and at this point the IT Team takes over, rub their hands with glee and an obsession develops on gathering all the data that can be found. Little consideration is given as to why the data is being collected, other than “it will help our marketing to be more effective” and planning what is going to be done with it once its gathered is often non existent. And with this data hunter gatherer approach, it seems that the sound statistical and analytical approach to extracting knowledge and insight disappears and in consequence it becomes easy to produce results from looking at the data that at best can be skewed an,d even worse, can be totally unsound. And there is also an implication that big data with its big servers and big software will cost big bucks so making it an exclusive tool for the large multinational Company, leaving us small and medium enterprises behind.

I would argue however that what is really needed to make best use of the data available is not Big Data but Smart Data. Concentrate on looking carefully at what data is available, extracting only data that is potential directly relevant and then using sound statistical sampling techniques to analyse and unlock the insight and knowledge within the data.

In a sense the term “Big Data” is a strange term as it is used to imply that size is in some way a measure of quality and value and I believe in a few years time we will find it odd to talk about big data. Size in itself doesn’t matter – what matters is having relevant data that helps us solve a problem or address the questions we have and so I prefer the term “Smart Data”. Smart data is available to all organisations small and large. It can use existing tools and techniques to evaluate and gain insight.  And when we want to bring in the additional data sets that the likes of social media and Google Analytics offer us then the way to do this is through creating and integrating small data sets using the techniques and systems we already have in place and not through building big data monoliths and creating massive centralized data warehouses.


Is the “Net Promoter Score” a good measure of Customer Satisfaction?

We are all incessantly being asked, at every turn, to undertake surveys or rate an experience, with the stated objective that as a valued customer we can provide feedback to help improve the experience for us and others in the future. Part of this need for a “pat on the back” from our customers is the fear that our competitors will be receiving more frequent and higher ratings, something I call the Trip Advisor Syndrome. And more often than not the comments that accompany this rating are ignored for the most part as the advice for improvement is not practical or possible, given a combination financial restrictions and entrenched methods of service delivery. Further the number and mix of questions beings asked of the customer make sound results from the customer feedback difficult to evaluate at best which in turn can lead to flawed knowledge being derived from the data.

Customer Service Survey

Over recent months however, DDL Insight, the analysis arm of DDL Group, has seen a rise in the use of the net promoter score,  a methodology quite widely used in the USA, as a measure of rating customer experience. It’s very simple to implement, in fact it consists of asking one simple question and the manner in which this question is asked cannot vary as the would invalidate the outcome of the measure. 

The question asked to obtain the net promoter score is:-

How likely is it that you would recommend our Company to a friend or colleague?

The answer is in the form of scoring from 0 to 10 where 0 means not at all likely and 10 means extremely likely.

The answers to this question are the grouped into three categories:-

  • Promoters. These are customers who scored either 9 or 10
  • Passives. These are customer who scored either 7 or 8
  • Detractors. These are customers who scored between 0 and 6.

To calculate your Net Promoter Score, you take the percentage of customers who are Promoters and subtract the percentage who are Detractors. Simple! You end up with a score between -100% and +100%. A score greater than 0% indicates more customers are wowed by your service than alienated and vice versa. The higher the score the better the overall service.

So what are the advantages of this approach?

  • It is simple, no need for lengthy surveys, no need to ensure leading questions are not asked, no need to worry how quantitative measures will be generated from the combination of questions asked. And its simple to come up with the score.
  • It should improve response levels. It’s quick for the customer to complete and its a question we all understand.
  • It ignores the passives. When we really don’t have strong views, good or bad, in relation to our experience we are likely to score a 7 or 8. We don’t want to upset the supplier by sounding negative but equally we were not vowed by the experience and so would not score a 9 or 10.
  • Over time you can monitor how the score changes. If its going upwards your service is getting better overall, it its going down, your service is deteriorating.
  • You can compare your score with other similar Companies, such as competitors. In the USA it is common for the score to be published, this is a trend that is also growing in the UK. And because the question and method of scoring are identical then comparison against similar Companies with similar customer bases is possible.

It is not a perfect method of measuring satisfaction and indeed this method of measurement does have its critics. It does not measure all aspects of the customer interaction, for example the propensity for loyalty, and the figure is less reliable with small numbers of responses, as with any statistical gathering. However I do believe that the increasing desire for customer feedback will eventually alienate customers and so anything that can keep this simple from the customer perspective will prolong the time frame over which valued information can be derived.