Often large and complex data sets are difficult (at the best of times) for brands to capture, manage and process within traditional database systems.
According to a report by Mckinsey Global Institute, too many companies are missing opportunities to cash in because of a lack of data management expertise. Glaxo Smith Kline's (GSK) CRM consultant agrees that firms need to invest in operations to process big data immediately. Similarly, UK retailer Wickes head of CRM, adds that you need to be focused on what you want to get out of big data, or you can spend too much time analysing it. One needs actionable insight.
Amazon is a prime example of a company leading the field in terms of collaborative filtering technology which allows it to automatically recommend what customers should buy based on their purchasing history.
Segmenting the data, rather than analysing reams of information allows for a focused approach and this can be tunnelled into a powerful strategy to more effectively target customers and thereby personalise content.
While big data allows retailers to more effectively segment and market to their customers, the same information is also being used by price-comparison businesses to push down prices in the market.
Any downward pressure on process will be fair if big data enables brands to target consumers more effectively, if people allow their data to be used in marketing and see a benefit, then more effective data will be submitted and this in turn will help firms to focus their marketing creating a win-win situation.
Firms need to implement best practice in handling data when dealing with so many possible sources and the implementation of the POPI Act in South Africa will make companies find more creative ways to reach consumers.
When big data is done properly, it can create immense value. However, if one is not already dominating a field and mastered all of the basics, then any diving into big data is just chasing a trend and wasting precious time.
A few key considerations
Steps to effectively implement a big data strategy
Start with random: Try making recommendations for other products randomly, it will still work better than not making recommendations at all.
Recommend your best-selling products: or best-selling service of most read content. Take it a step further by recommending the best-selling that the customer hasn't already purchased.
Take a single product view: this should be simple. Look at every customer that has bought that product or read that content and then look at the second-most popular content/product and recommend that.
Generally the returns of implementing big data strategies are not as fruitful as you would like to think, despite an organisation's level of skill improving with each new campaign. Amazon is a good example of a company that has managed to understand and leverage big data, with a 0,1% return. However, in relation to the size of their service-offering, your company may yield far smaller results.
Similarly, hard core analytics and big data can add value, but only marginally and only for companies that are already using their data effectively.
