Marketing and sales is becoming an increasingly data-driven discipline, and more effective use of customer data through customer analytics has become the key to improving customer experience through personalization as well as improving marketing ROI.
However, consolidating vast amounts of customer data from multiple sources (CRM, social media, web, mobile, etc.), determining patterns and leveraging business value from these may seem to be a daunting task. This is where customer analytics has become one of the most powerful analytics tools.
With customer analytics, companies can aggregate internal/external and structured/unstructured customer data from multiple different sources, yield customer insights, personalize offers and understand marketing performance in a way that could never be achieved before.
Listed below are seven well-known topics within marketing and sales where customer analytics have a bottom line impact.
- Recommendations of items that could be of interest for the customer based on correlation between items, similarities between customer groups, external data sources (e.g. event, weather) etc.
- Increased cross-selling/upselling
- For example, data may show that 30 % of customers who buy product A come back to buy product B within six months. This information could be used to speed up the sale as well as convert those who might not have otherwise considered purchasing product B.
- Prediction of churning customers based on historical behavioral data.
- Warnings of potential churns, enabling sales and/or marketing to take action before it is too late.
- Leads scoring (hot, warm, cold, semi-warm etc.) based on historical online behavior and purchases.
- Better qualified leads handed over to the sales team through more accurate leads scoring.
Micro segmentation and targeting
- Micro segmentation of customers based on historical online behavior and purchases.
- More refined segmentation, enabling marketers to better understand their customers and increase customer experience with micro targeted offers/content.
Customer lifetime value
- Prediction of CLV for individual customers based on historical data for similar customers/customer groups.
- The CLV predictions can be used for more accurate forecasts of future revenues, better understanding of different customers, etc.
- Analysis of external data streams (e.g. social media channels) to track customer opinions’.
- Insight into what customers think of campaigns/new product launch, which can be used to optimize campaign and/or in product and service development.
- Calculation of the impact of different channels/touch points (social media, email, call, display, etc.) on the conversion.
- Identification of the contribution of each channel/touchpoint to the conversion, which can be used to calculate marketing ROI of each channel and better understand the performance.
By putting these use cases into practice companies have achieved major impact on the bottom line. Some success stories are:
- Amazon generates 35 % of their annual revenue through personalized product recommendations.
- Starbucks uses customer loyalty cards to collect purchase history of customers (what was purchased, when and where), enriches it with external data (weather, local events, inventory, etc) and applies segmentation and recommendation to provide customers with personalized offerings.
- Samsung detected and counteracted customer dissatisfaction with a newly-released S8 smartphone model through sentiment analysis.
Before making investment in systems that carries out these use cases, however, companies must prioritize these use cases based on potential value and strategic fit. Thereafter, it should be tested if, and how, the selected use can be applied to the business, which is most effectively done by building a prototype.
Please do not hesitate to contact us if you want to discuss how customer analytics should be applied to your business. Send email