So - you’ve been using some analytics, and you’re wondering what to do next. You’re not the only one! Sometimes it seems like everyone is chasing after the end of the analytics rainbow. If you’re a manager or a user of analytics insights, maybe you’re wondering what else your analysts should be giving you to look at. If you’re an analyst maybe you’re wondering about logical “next steps” now that you’ve been providing value to your organization for a while.
Below, find a short description of a wide variety of projects that you could undertake to provide more analytics value. While it’s by no means exhaustive, it’s a good place to start.
Churn Modeling helps you to identify which customers are likely to leave within a defined time period (this is called customer churn). This provides the foundation for developing customer retention and loyalty marketing initiatives to target customers before their tipping point, minimizing the impact of future customer churn. Churn rates need to be expressed as a financial consequence to the business, to help determine the budget for loyalty initiatives. Click here for more information
Customer SegmentationCustomer Segmentation provides a framework for monitoring the effectiveness of your marketing strategies, identifying opportunities, making more relevant offers, and developing products or offers. Divide and conquer your customer base in order to understand your customers’ needs, see their differences, focus on those with the most potential, and ultimately turbo-charge your business. Datamine develops bespoke customer segmentations for our clients using any combination of characteristics such as geographic, demographic, psychographic, behavioral, product and profitability. Click here for more information
A Knowledge Discovery (often called exploratory data analysis) is required any time you want to capitalize on a thorough knowledge of your customers, identify your key business challenges, and get in the best position to act on opportunities. A Knowledge Discovery extracts the useful knowledge from data using analytics to provide an understanding of your customers. Extensive customer profiling, time-series investigations of trends, and analysis of unusual patterns is usually undertaken.
Media Effectiveness and Optimization
Media Effectiveness identifies which of your media placements are working and which aren’t. Media Optimization takes this information to determine the best ways to use your marketing budget. This allows you to put your emphasis on those activities that influence customer spending for the best response and return on investment. To do this, you need to tie your media spend to actual metrics like sales, acquisition, and customer churn rather than media-based ones such as TARPs or reach/frequency. Click here for more information
Price Elasticity and Optimization
Finding the optimal price point for products can significantly drive business performance. More elastic products rise quickly in demand as the price falls and less elastic products rise slowly in demand as the price falls. Finding the optimal price point requires analytical input as it differs based on business objectives (margin, stock levels, attracting customers, supplier relationships). Click here for more information
Merging disparate databases and storing the data in a single repository gives you a more informed view of your customers. Easy access to data speeds up project delivery time-frames and allows for the identification of otherwise unseen opportunities. The data is regularly refreshed and cleaned so it is on hand for leveraging in analytics. While most databases are built to make getting data in as efficient as possible, analytics requires building a database where getting data out is the priority. Click here for more information
Estimating staffing requirements and organizing rosters can be a costly exercise – as can getting it wrong. Over estimation results in unnecessary overheads, while under estimation can result in poor organizational performance. Demand Forecasting models project staffing requirements for a given period of time.
Whether you’re a market leader or a follower you have a share of the market. Market Share analysis looks at your transactional performance versus the total market potential by analyzing transactional sales volumes, transactional values, transactional numbers and market share. This information, and the underlying trends, provides insight that can transform your business decision making process. Click here for more information
Share of Wallet
How much of a customer’s wallet is dedicated to spending with your business? Share of Wallet is used to determine what proportion of spend your customers have with you vs. other services or competitors. The analysis helps identify the types of customers likely to have low or high share of wallet. It is then possible to profile their product usage, as well as frequency of spend and contact, to reveal the products you can expect to sell to a customer. Having gained this understanding, campaigns can be developed that are aimed at increasing Share of Wallet. Click here for more information
Store Site Assessment
Store Site Assessment is used to determine the potential profitability of a new store location. This is often done if an organization has a multitude of stores and wants to determine the best location for a new store opening. This potential location is bench-marked against existing store locations to give an idea of the viability of the location based on potential catchment areas, market opportunities and potential cannibalization of existing stores. Click here for more information
Post Campaign Analysis
It’s critically important to analyze responses to campaigns to ascertain the characteristics of the most and least responsive groups, as well as the cost-effectiveness of the campaign itself. Reports can be tailored to business needs or be all encompassing and on-going. Many organizations forget to measure the outcomes of campaigns or get too busy to do it properly.
Profitability analysis is developed specifically to understand the profitability of your products, services, channels and customers at the most granular level. It also provides essential information for driving strategy, creating scenario models, and understanding how business decisions are affecting the bottom line. We often take a more practical approach by initially approximating value to give relative profitability, as the fine detail of this type of project can be complex and political.
No two customers are the same, so understanding the different types of buyer behavior patterns can assist in identifying different groups of customers and their needs. Customers buying behaviors can be analyzed to identify; those who will remain single buyers, those with multi-buyer potential, and which products they are likely to buy in the future.
When a business is in acquisition mode it is important to remember to attract the right customers, not every customer! Predictive Acquisition Modeling ranks prospects based on their similarity to existing valuable customers to target those who are most likely to respond and become valuable customers.
Customer Cross Sell
The key to customer cross selling is targeting customers with relevant offers at the right time. Predictive Cross Sell Modelling can be used to identify who should be targeted and when. Both timeliness and relevancy are important in effective cross sell offers.
A customer likely has many contacts with the business throughout the customer/business relationship. Contact Programmes manage all of these contacts in one system taking into account variables such as value, trigger events, risk, channel preference and the status of their relationship with your company.
Businesses run a variety of campaigns often targeted at different audiences, at different times, and at different stages of the buying cycle. Campaign Management ensures all of these campaigns are cohesive and recorded with continuous performance assessment to optimize future communications.
Operational EfficiencyUsing analytics to help drive operational efficiency in logistics can result in big cost savings and better service levels. The first step is getting together some historical data so you can identify areas of inefficiency and model new processes. This means everyone has to be aware – don’t throw away your data! Too many organizations purge their most granular (and valuable) data because someone told them not to keep it all – a hangover from the days when data storage was expensive.
If you’d like to learn more about our solutions or have a chat about analytics in general, please feel free to get in touch.