5 mistakes to avoid in data-driven marketing

Even though the advent of data-driven marketing happened with the sole focus of lead conversions and increase in ROIs, the conversations around this subject are finally skewing towards the real purpose of it- better customer engagement. Building customer loyalty had become a lost cause for most organizations as millions were spent in capturing the data and then analysing it only from a sales-eye. However, trends are changing. Digital being the foremost medium of data collection has without a doubt disrupted the sheer quantum of data that can be captured and studied. But in an emerging market like India, where the consumers are still experimenting with their data consumption, data-driven marketing is yet to take a definite form.

In a recent survey, it was found that more than 80% of the Indians surveyed found the pace of changing technology, too quick to their liking. You see a stark contrast when you compare the same number with ~40% of UK respondents. This gives you a sense of shifting paradigms in Indian consumer businesses vis a vis a matured market like the U.K. Thus, in a dynamic market like ours, what are the challenges that marketers face today w.r.t data? Rather, what are the mistakes they must be wary of?

Let us find out-

Not having a cross-channel strategy

Let us start with an example. Hypothetically if you are amidst a new product launch, there are different teams engaging with customers on varied platforms and channels and of course, collecting data. Firstly, if your marketing strategy has to culminate into a logical conclusion in the end, it is imperative that the different channels talk the same language and target the same set of pre-decided customers. Secondly, the data collected can not and must not be analysed in silos. There needs to be a constant dialogue between the various channels.

Focussing on all the data

Some organizations can be so focussed on the data-collection part that this in itself becomes their target with all the sense of purpose on the analysis being lost. Today Big Data is being employed to collect humongous amounts of data points which quenches the thirst of a big sample size to begin with. But you need to look at data intelligently and in fragments such that amount of data is not overwhelming but at the same time makes sense to your strategy.

Sporadic data collection and analysis

Continuing the abovesaid example, it must not be an event such as a new product launch that triggers the data collection process. Data processing needs to be a continuous process for any organization. It should be used to re-target customers, better customer experiences and for re-launches as well.

Losing sight of the end result

If it is the ROI you are after, make sure that all the data analysis ultimately points to a change in it. Similarly, if better customer engagement is the aim then the data collected should have the purpose clear. It is very common to lose sight of the purpose and get into the process of hogging data. Data collection, segmentation, analysis etc. must each have its aim clear.

Ignoring the expertise

Data-driven marketing is not just about producing copious amounts of data points or reports and just tracking them, it is also about using expertise and technologies to derive at better results, faster. For example, it is advisable to have data scientists who have statistical modelling experience along with HTML, SQL etc. Similarly, there are technological tools available in the market that can be deployed for a matured analysis.

Having said the above, today’s digital customer is playing hard-to-get. The most likely of customers who are the most avid consumers of digital data, are the ones with maximum trust issues when it comes to brands. Consumers are turning off cookies, switching adblockers on and denying data-sharing with most companies thereby resulting in loss of data being captured. Hence, in a nutshell, the customer that an organization wants to target the most refuses to consume the advertisements. All the more reason why our data-driven marketing strategies should focus on building customer’s trust and loyalty rather than wanting more lead conversions.