How businesses can grow with data analytics and predictions for the future
The volume of data available to businesses is ever-growing and with this we see (increasingly) the value and importance of big data analytics in harnessing this commercial asset and contributing to business success and growth.
Big data analytics is the art and science of harnessing huge volumes of data and uncovering valuable nuggets of information that a business can use to empower insights and support their strategic objectives and ambitions by putting data to work.
Big data analytics is important because the benefits that a business can make through the smart application of its big data can be wide-reaching in terms of generating growth and enabling massive operational efficiencies that drive up profitability.
Central to this is the power of big data to help businesses better understand their customers. The better you know what your customers want, understand how and when they want to buy, and do this through an experience the customer loves, then the more the customer will want to shop with you over your competitors, increasing their loyalty and brand advocacy.
Generating insights from your big data enables you to put customers at the heart of what you do, grow the business and create efficiencies that drive costs down and increase revenue.
Data science and strategic consulting experts, Beyond Analysis, consider the five areas where the benefits of implementing big data technologies and putting data to work can typically be found.
Due to the far-reaching, extensive nature of big data it allows you to understand patterns in customers’ purchase behaviors and product choices, for example to identify where customers have ‘holes’ in their shopping baskets. By understanding what products customers might buy if they became available or identifying their alternative product choices, enables businesses to evolve their product line and up-sell. Commercial teams can use these insights to supercharge their ranging and promotional strategies. Likewise, changes in purchase patterns can be early signals of customers switching to competitor brands and the CRM team can swing into action with remedial actions and marketing tactics to retain customers.
Data is generated every time a customer makes a purchase, clicks on a web page etc. and together these data footprints can be used to generate patterns of behavior. Using additional data sources, such as product metadata, data scientists and analysts can model behavior to help predict and identify the needs and motivations behind purchases. An example of this might be that a customer that only ever buys ready meals may be classified as someone who is time-poor and not interested in cooking. These insights can be powerful in developing product design and development process, to keep your products fresh and meeting the latest needs of your customers.
Customer data, be it the route they have taken through a website before they make a purchase or drop-off, their social media posts, in-store transactions, or their click-through rates on marketing communications, offer powerful insights into what customers enjoy about a brand and what is not working.
