A great example comes from consumer brands born on the Internet, such as Alibaba, who excel at market basket analysis - analysing customer purchasing behavior to figure out what they might buy next - but it is relatively new in the B2B space. Basically, you can determine which accounts have the greatest propensity to buy based on which purchases are likely to go together. Retailers use it for promotions and targeted recommendations.
Tip 2: Apply data analytics and build your "data bench"
It takes more than having the right technology to be successful. Having the business perspective to develop actionable insights for improving the overall customer experience is essential. Organisations need to employ the right people who can merge their data analysis skills with their inherent curiosity and creativity to correlate data sets that seem unrelated to help uncover new insights. There are three or four roles to consider:
- Data analyst, who is familiar with how to extract and transform data for its intended purpose
- Data engineer, who knows how the data is being captured, which servers it is located in, what tools are required to extract data for analysis
- Data scientists, who can create a profile or do clustering analysis on data
- Segment experts or consultants, who can contextualize the findings and deliver recommendations that are compelling to senior business leaders
Ultimately, to use data effectively, organisations need to have a vision for what they want to achieve and how they intend to become a leader in customer experience. At the core of every customer analytics journey, the goal should be to anticipate business needs, simplify redundant processes, and understand the customer. Only then can companies truly embrace Big Data to address customer issues before they become problems and to increase value for the organisation.
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