Most organizations have a treasure trove of data and therefore the basis for gaining intelligence that leads to stable, increased or new direct or indirect revenue.
The data is often contained in a Customer Relationship Management System (CRM), an Association Management System (AMS), a Subscription Management System (SMS), in Sales Software or basic databases and spreadsheets. Staff throughout organizations have leveraged the technological opportunities available to better track and manage their interests, their customers and the information they need to do their jobs. The valuable and insight producing information is usually managed within departmental stove pipes, exists unconnected to the curating efforts of others in different systems and fails to offer an organization the strategic advantage that they need in today’s hyper competitive business environment.
Data=Revenue Data=Intelligence Data=Performance Data=Satisfaction
The stove piped focus results in curating and collecting specific customer and transaction data needed by a department but is not inclusive of all of the possible data (information and behavioral) that could be used across the organization.
The amount of data and the promised potential for learning insights and gaining intelligence is a new concept for most. Technology and the systems now used weren’t previously available and most processes were paper based. Analyzing data contained within stacks of paper, journals and possibly receipts is arduous for the most ambitious, is incredibly time consuming and prone to errors and also challenges our short attention spans. We are only able to retain so much information in our short term memory and of course, the short duration of what data is retained doesn’t allow for future referencing.
Technology has solved many of the previous process challenges. Those in an organization don’t need to retain huge sums of information because that information is now contained in systems and is available with a few clicks of a button. Reports can be generated and dashboards created to provide views into the data (aka information about customers, their behaviors and the business) further enhancing the process.
Lets Talk about Analytics and Data and Why Direct and Indirect Revenue may be left on the Table
Data grows by the second and at rates inconceivable a brief 20 years ago. Data can now be mined to produce business analytics including descriptive, predictive and prescriptive.
Types of Business Analytics
Descriptive Analytics: Current management reporting focused on past performance or sales and marketing, finance and operations reporting, financial statements and IT performance.
Predictive Analytics: Seeks to “predict” what will happen. Predictive analytics uses descriptive data (historical, past performance), often includes external data such as socio-economic, demographical, regional and market (local, national and global) and relies on models, rules and algorithms to solve business problems and produce “predictions”.
Prescriptive Analytics: seeks to build on predictive analytics and outcomes to determine what will happen, when it will happen (predict) and why (prescript) it will happen. The “why” is the most important knowledge gained. Knowing the “why” provides an organization an opportunity to make corrections and changes to impact or avoid the prescribed outcome. Prescriptive like Predictive relies on models, rules and algorithms including Decision Support Systems (DDS).
Business Analytics, representing a sub focus of data science, can be defined as the science of collecting and analyzing structured and unstructured data to derive conclusions, knowledge and insights. The current term “big data” is all over the news and many struggle to understand what it represents. In simple terms “big data” brings together all previously existing structured data types and sets (financial, operational, performance, transactional as examples) and newly available unstructured data sets (video, digital transactions, social, comments, sentiment, audio, behavioral as examples).
If an organization is mining its data via reports and interfaces, it is most often a “descriptive” exercise looking backwards to see what happened and why. The descriptive exercise is helpful to understand how you performed and perhaps gain insight into why goals were or were not met. In a dynamically changing marketplace that is further challenged by changing demographics and resulting preferences, descriptive isn’t enough.
Collecting customer and business data (pieces of information) and applying predictive and prescriptive rules, models or methods enables an organization to use the result for decision making, new initiatives or course adjustments.
Perhaps you want to grow your customers, members and subscribers and have previously relied on marketing campaigns to create awareness about your products and value proposition. Was the performance data from the campaign linked with your customer database (CRM, AMS, SMS or other)? Are you collecting more than name, address, purchase date, product purchase type, and customer support activities? The organization probably is but additional pieces of information may be resident and managed in those other systems I mentioned.
If the organization is maintaining deep information about customers and the business, are analytic models, rules and algorithms being applied to deliver insight and intelligence?
If not, you are leaving revenue on the table and missing an opportunity to predict and prescript future performance.
It is critically important to know what data is resident in your organization’s systems, in silo and unconnected systems, data tracked by individual employees and what data is available via external means (market analysis report, industry survey, government data).
An assessment of what you have is necessary to determine what data exists, where insights can be gained and what data you may need to predict and prescript current and future performance and direction. You may need help assessing your currently available data, determining what additional data you need, and how to gain insight. The help you need may not currently be possible with your existing staff.
The Current and Future Opportunities
Identify profile and personalization opportunities to deliver relevant information, products and services to the right customer. Determine the “Right Customer” at the “Right Time”.
Define groups and segments to set strategy and meet demographical and geographical needs.
Determine customer similarities in purchasing, engagement and consumption. Match demographical and socio-economic external data to gain even deeper intelligence.
Identify churn, are there similarities in those customers? Are there commonalities? Use to develop strategies and tactics to reach your most at risk customers and protect revenue.
Predict your future financial performance mining your descriptive (historical) data and use models to predict future behaviors.
Gain insight into your current cash flow. Review how and when customers pay, when they renew and by what types of payment.
Determine what individual customers are buying from you. Are there themes?
Use internal and external data including descriptive data (historical) to validate forecasting assumptions across your operations, programs and product areas. Are the forecasts realistic?
Your VP has an idea for a new product or service. Does your current customer information help determine if it is something desired by your customers? Why invest millions for something your customers don’t want.
The list of opportunities is by no means exhaustive. You have so much readily available at your fingertips with the right tools, approaches and strategies. Give us a call and we can help you take advantage of data and analytics and put the direct and indirect revenue in your hands and not left on the table.