How to Derive Insights From Data Thrown by an Analytics Tool

Santosh Kumar
New Update
How to Derive Insights From Data Thrown by an Analytics Tool

With the evolution in digital advertising and increasing spends, the demand for right measurements have grown up high. Hence many analytics tools have come up throwing thousands of interesting data points. However, the most important thing for client is to take meaningful information out of the data thrown by tools to take informed decision.

Today, Big data is a buzz word and every organisation is trying to latch on to it. The retail giant, Walmart deals with more than a million transactional data every hour and imports it into databases estimated to contain a whopping 2.5 petabytes of data (2.5 quadrillion bytes)! Surely, most, if not all, of this mammoth data trove contains rich insightful information; waiting to be harnessed into decisive actions.

I have come across some clients and agencies who have subscribed to multiple tools and probably the best tools in their specific area to get data but they have still not been able to derive actionable insights from the same. Remember, big data is a lot of data and only good analysis combined with large information can lead to effective and actionable insights.  Therefore it is important to not only invest in right tools but invest in developing good analytics as well.

Here are a few tips on how to derive insights from data.

Clearly defined objectives

Any tool provides many data points but it is important to know which data points are more important to you and what is their relevance in your final objectives. Hence clearly defined objectives will help you analyse data in context of goals and objectives.

Selecting the right tool

Today, many tools are available in the market. Some are specific to one medium and some are universal, throwing data points covering larger spectrum. It is important for you to know which tool will provide relevant and specific data required for your organisation or brand. Hence, take a demo of various tools that suit your requirement before zeroing down on one.

Integrate data sources across platform

Seeing data in silos will lead to wrong insights and does not portray the right picture. It is required to set-up complete analytics from reaching out to consumers, conversations, coming to your brand assets, user journey to final the goal. Eg: Analytics on your twitter campaign without having context of other campaigns and website performance will not provide actionable insights. It will also help you in find the right attribution model to get most relevant sources working for you.

Integration with offline

We always have to correlate data with offline campaigns. Be it brand mentions, conversations , increased search or direct traffic, the integration with offline campaign always provides more meaningful and relevant insights which not only help in making digital campaigns better but also improve your mainline communication.

Segmentation of Data

The most critical part of analysis is to segment data and go deeper in each segment. Segmentation can be based on audience behaviour, content, product or brand related data. Goal setting should be done for each segment and accordingly insights should be used to improvise ROI.

Bucketing of audience data

Based on behaviour and intent, audience segmentation can be bucketized to see deeper insights of each segment. Take broad to narrow approach. This audience bucket will provide rich insights like loyalists, enthusiasts, early adaptors, influencers etc. Also, it will provide rich psychographics to know your audience better. In DQ, we have done 30,000 + micro audience buckets combining their demographic & interests with behaviour and intent.

Personalisation of content

Today, consumer does not pay attention to communication which is not relevant to him. In a day, a consumer goes through more than 1000 communication points and the average attention span is around 5 sec. Hence, it is critical to make your communication relevant to the consumer. With audience segmentation, the content can be customised and more relevant which again offers lots of valuable insights.

A/B testing

Just analysing data thrown by tools doesn’t provide deeper insights until and unless we do A/B testing on our communication and see the change in insights. It will help in knowing the audience better in terms of what they want and how they respond to our communication. On an average we use 5-6 communication tactics among segments to get insights on what attracts the user and accordingly design the future communication.

History is goldmine

The historical data is goldmine when it is correlated with real time data. It not only provides bigger picture of audience reaction and their behaviour but also helps in predictive analysis of consumer response in future. It saves a lot of money and gives direction on deciding the right communication to the right audience.

Repeat the cycle

The traditional but effective approach. Get the right insights, derive actions from those insights, analyse the results again and leanr. Repeat the process again and again.

Integration with offline Integrate data sources across platform Clearly defined objectives Bucketing of audience data walmart data analytics tool Big Data insights Selecting the right tool Segmentation of Data Personalisation of content