Big data or large and complex data sets are an integral part of marketing. Marketers frequently use them when gathering and scoring leads.
Big data provides more accurate results for the derivation of trends and analysis and provide the results in a more comprehensive manner than separate smaller sets of data. However, it also poses significant challenges. How marketers step up to these challenges will determine the success of their engagement and nurturing initiatives.
A big challenge for marketers is making a transition from managing different channels separately, complete with their own budgeting and stand alone strategies. Big data facilitates a multi-channel approach, but marketers need to learn how to apply it well.
Marketers need to track the relevant data, whether it is an email click, a SMS response, a Facebook share or something else. The sheer volumes and opportunities made possible by big data can tempt one to lose focus or indulge in analytics for the sake of it.
Big data allow marketers to zero in on a consumer’s preferred channel, or the channel that influences the consumer most. This, however, is not set in stone and changes depending on the stage of the marketing lifecycle. For instance, email may be the most preferred channel for a welcome or thank you message, and customers may regard it as a positive engagement experience. However, the same customer may regard a reminder email for not having visited the website for more than sixty days as spam. The customer may regard a Facebook post providing a special offer as a positive engagement experience instead.
Finally, the generic challenges related to capture, storage, search, share and analysis apply to marketing big data as well. Many marketers consider the resolution of big data challenges as the job of the technical team. But the responsibility is on the marketing team to provide the relevant information, such as which data is important and which output they require with clarity. They also need to take the initiative to “clean” the data of errors, inconsistencies and duplications that can distort trends.
What challenges do you face by utilizing big data?