Predictive Analytics for CLM (Customer Life Cycle Management)
Predictive Analytics for CLM helps provides intelligence from customer data. Actionable insights into customer data are highly relevant for forming business strategies. These processes are used for gaining new customers and sustaining existing customers. Most firms have built systems to gather customer data but still lack proper analytic models. All the stages for Predictive Analytics should be in line with the customer’s chain of action for interaction and engagement. Most sales strategies aim at volume growth but miss the larger picture of long-term profitability. Predictive Analytics fill the gaps to make complete customer profiles which give sustainable growth models.
Requirements for Predictive Analytics for CLM (Customer Lifecycle Management)
Different stages of Customer Lifecycle require different Analytics set up. There are few points that the analytics professional must study before applying them:
- At every point in the customer lifecycle, the cost of an analytics intervention differs. At some points, analytics provide stronger insights into customer behaviour, it is more important to employ analytics tools at these points rather than stages where costs may be high but with little actionable insights.
- There are agencies and consultant analytic services which can be partnered to learn the amount of Analytics required for getting the desired objectives.
- Use existing data first. Before new data is analysed, importance should be given to the already collected data. It can provide opportunities which may have been overlooked in the earlier set up.
- The analytics process model should deal with critical customer processes first and not apply supplementary tools in the beginning.
- Assemble the right team. A core team of data scientist, business analysts, and technology experts helps guide the Analytics Process.
Aspects of Predictive Analytics for Customer Lifecycle Management
Acquisition Analytics: Segmentation and Targeting
As a first stage, it is imperative to study what motivates the customers to buy. Accordingly the promotional campaign should be designed:
- Studying existing customers. It is a basic and high yielding step to analyse existing customers and gain valuable insights. Also, by studying their changing perspectives new strategies can be developed.
- Better lead management. Once the marketing produces leads it is important that they are managed effectively to get the best conversion rates. For e.g. in every organisation, some agents have a better conversion rate or are better equipped to cater to certain customers, their optimised utilisation will help conversions.
- Customer profiling to increase sales. By studying existing customer profiles predictive analytics can point out similar market areas for higher conversions.
Optimisation of Offers and Promotions:
Predictive Analytics enables the right promotions and offers at the right time to develop customer relationship. Offer optimisation on gathered customer responses helps in conversions as well as the loyalty of existing customers. For e.g. during some seasons, there may be a higher likelihood of getting customer response by giving offers and promotions.
Usage Campaigns and Spend Analytics:
Once customers begin using products their feedback is invaluable for the development of future versions.
- Different Channels are used to reach to the customer. Therefore, it is important to analyse these different channels and calculate the ones giving the highest ROI and accordingly divide spending on these channels. Also, some companies selectively release products to high-value customers and gather their feedback before going for a full release.
- Social Media and public internet platforms provide rich space for gathering data on customer experience. Although it is still difficult to gather all the data, it can give direct and quick results to how consumers are finding your product. These platforms can also be used to interact with existing and potential customers.
Revenue Optimisation is a conclusive outcome of different analytic models for efficient business strategies. Revenue Optimisation should be taken as a journey starting with data already at hand and setting up new channels at important points.
- Customer Intelligence is developed over a period of time by the data collected and filling in the gap by additional channels.
- Analysing which segment will yield more profit and apply more Analytics in that area.
- Differentiate customer education with customer engagement. Both are different aspects. When marketing complex products a preliminary educational campaign is important to raise awareness. Education marketing helps in creating value of the brand in the customer’s minds or helps them to narrow down their choices.
- Engagement stage requires trials, reviews, community interactions, and a call to action.
- Building new customer relationships is as important as keeping existing customers loyal. This should be done around the main values of the organisation and not only by increasing the customer base for profit.
Customer Lifecycle Value Management
Value can be improved through implementation of Customer Lifecycle Value Management model. Customer segmentation is necessary to ascertain the future customer value and the right application of appropriate models. Various input channels have to be analysed to find the proper solution. In the case of online portals re-marketing has been effectively used to make special offers to customers to avoid defection.
Cross Sell and Up Sell Analytics
As the customer relationship grows, by behaviour analysis more offers can be presented. According to the history of customer and firm interaction new products and services can be offered and once their response is analysed they can be generated as automatic recommendations for other customers as well.
Usage Campaigns and Retention Analytics
For the continuation of CLM, it is important to employ retention models.
- Customer patterns can help predicting future customer actions and accordingly steps can be taken to ensure retention.
- One key point for customer retention is the comparison of perception and actual experience, the customer will remain loyal as long as the experience is greater. The main areas are – order fulfilment (Delivery efficiency), product performance (error free usage) and after sales service (response by service and technicians). These points are most worked upon by all companies, therefore, analytics is required to prioritise and have new ideas in these areas.
- Developing Loyalty Models helps to keep the customers engaged. They are used in tandem with profitability model and retention models.
- Loyalty schemes translate into having higher rewards when customers spend more.
- Loyalty models have also proved to be costly therefore it is important to have proper customer analysis before making special offers.
Facilitation of Predictive Analytics
In order to make Predictive Analytics effective, some points need to be taken into consideration.
- Enforceability – It is important to give the Predictive Analytics Team the power to enforce their ideas to drive business, as using analytics for only reporting will be a redundant exercise.
- The speed of Delivery – The timely delivery of relevant actions is important because of the short customer attention span.
- Clear Communication – Many of these strategies may be complex in the beginning but clear communication within the organisation is important for understanding.
- Accuracy – Analytics also need to have a high degree of accuracy to show confidence on their enforceability.
Predictive Analytics is an essential resource for growth and sustenance of businesses. With more channels to gather customer data, gaining actionable insights is the next necessary step for businesses to grow.
Keywords: Predictive Analytics, Advanced Analytics, Analytics, Customer Lifeycycle Management, CLM, Sales, Marketing, Sales Strategies, Customer Intelligence, Acquisition Analytics, Segmentation and Targeting, Promotional Campaign, Lead Management, Offer Optimization, Usage Campaigns, Spend Analytics, Revenue Optimisation, Cross Sell Analytics, Up Sell Analytics , Retention Analytics
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