Why big data fails to provide big insights

In an increasingly global and competitive marketplace, no business can afford to ignore data about their customers. The growth of information technology has transformed market research, with a growing number of businesses learning about customer preferences and buying habits by mining massive sets of quantitative data. The buzzwords in market research are ‘advanced analytics’ and ‘big data’, but are these data analysis methods able to cover everything? Are traditional methods of data gathering no longer required?

Big data

Big data has existed for a long time now in organisations. At its most basic level, it could be considered the database of customer transactions. Some information gathered from big data includes: what customers purchase, when they purchase it, what they purchase along with it, their purchase loyalty, and their history. At the most sophisticated level, this can include massive data warehouses that collect real-time customer behavioural data, as well as dozens of other information streams from both internal and external sources.

Advantages of big data

Today’s relatively inexpensive computing power and data storage solutions have opened up new and more efficient analytical possibilities. This means businesses can apply all of the advanced analytics required to mine the data, predict behaviours, and implement real and practical business strategies. People are also becoming more comfortable with sharing their lives online. They are sharing more demographic information about themselves that can be collected and analysed. Big data also allows researchers to have shorter and more relevant surveys, with a focus on ‘why’ rather than ‘what’.

Disadvantages of big data

There are several disadvantages of big data, however, and businesses have to tolerate a certain level of inconclusiveness when working with it. It has a very strong bias towards quantifying behaviours and predicting future outcomes and results based on historical views. It tends to be only internal company data so ignores the competition. Plus, it lacks attitudinal information and psychographic data, as researchers can see what customers do and predict what they will do, but not know the how and why.

Certain data is limited to those customers that readily provide access to personal information. Personal demographic metrics such as gender, age, and location can be misleading, as customers could have presented inaccurate information, or failed to keep it updated and current.

What’s the alternative?

What are customers thinking? Why are they behaving this way? What do they prefer and will they act if changes are made? How can I influence them to my advantage? These are the questions addressed by segmentation research, which pays particular attention to behaviours and motivations to identify meaningful segments. It identifies an opportunity, collects the needed information, then formulates an appropriate strategy.

Advantages of segmentation research

While big data concentrates on extracting predictive information about customers from large databases, segmentation research focuses on identifying factors that influence the buying decisions of customers. Relevant data can be collected through surveys, one-on-one interviews, observational research, and intercept surveys. Through this, researchers are able to get a better understanding of the consumer and their emotional triggers and reactions. With its focus on empirical figures, big data lacks the flexibility and versatility of segmentation research to find out more about the behavioural triggers or intrinsic motivations behind certain decisions made by your consumers. While it is all well and good to know what your customers are buying and how often they are buying, all this information is nothing but numbers on a spreadsheet. Finding out not just what your customer is doing, but why they are doing it, empowers you to tailor your strategies in a manner that is relevant to your consumers.

Disadvantages of segmentation research

All of this comes at a price, however, as data from segmentation research is often complex, and requires an analyst with both experience and expertise in order to tease out key data points which the company can then act upon. Most companies likely lack a person well-equipped for this role, and will have to outsource it to an expert. In addition to that, a comprehensive and accurate segmentation research project also doesn’t come cheap, so it is important for CMOs who intend to undertake segmentation research to have a good-sized budget, as well as hold the support of their management team (if not the entire company).

Big data and segmentation research face some limitations when examined as separate entities. Businesses need to understand that it’s not the existence of any particular data asset that ultimately matters, but rather the ability to answer pertinent questions about the business’ target audience. While the sheer volume of information big data provides is incredibly valuable, an in depth segmentation, when used in conjunction with big data, can provide a more comprehensive understanding of the consumer, which can only be good for a business.

Is your business struggling to identify consumer behaviours which could help you to further refine your marketing strategy? Get in touch if you’d like to find out how customer segmentation can help optimise your business.