Analysing data provided by smart business applications, such as CPQ and CRM, facilitates business forecasting and planning.
CPQ, CRM and back-office applications require specific data to perform their specific tasks.
Products like CPQ query and select part names, descriptions, prices, dependencies, co-requirements and other values that are manipulated via business rules, and user requirements are added to the system to produce configured products, orders, quotations or proposals.
CRM assembles campaign lists by gathering company names, contact names, titles, industry codes, locations and sales performance information. That information is then organised into data sets by matching specific attributes that are common to potential buyers or qualified targets.
The data in these two examples has been refined and normalised through data analytical processes to render it useful to our applications.
But what about data that is not neatly organised into tables? What about hidden data found within irregular text that is made up of everything from articles, blogs and books to correspondence and conversational transcripts?
Can we expect our business applications to go beyond sorting, categorising, sequencing, isolating and grouping actions? We need our systems to analyse free-form text and deliver insights to users.
In many instances, this is already happening. Let’s look at some examples.
When we task CRM to assemble a listing of companies that show high-growth rates, chances are the search will involve the comparison of annual revenue over a specific period of time. That kind of analysis is not magical, and unfortunately, it is not always fully accurate.
Enterprise growth rates derived from annual revenue numbers may include things other than product sales. Perhaps a struggling company sells off real estate assets or spins off a business unit to raise cash. That’s not a growing company; it is most likely the opposite. The high-level revenue number may well reflect substantial year-over-year growth, even though the annual sales of the company’s key product lines may be tanking.
What if CRM could access SEC filings, specifically 8-K reports that are related to “significant events” that occur within the reporting company? This is where mergers, partnerships, acquisitions, spin-offs, divestitures and other significant transactions such as layoffs, new product announcements or real estate sales are reported.
This is also where the “real story” is frequently found, rather than via press releases or raw numbers. Free-form text analysis is required to exploit this capability.
Predictive Market Dynamics
CPQ is fantastic when it comes to accessing and manipulating large quantities of highly specific data types to deliver product configurations, product availability, pricing quotations and even fully formatted proposals. Most of this data, while current, is not forward-looking; it is based on past conditions, perhaps as recent as a few minutes. However, they are still in the past.
What if CPQ could reach out beyond the confines of the enterprise to look at and interpret raw data related to geo-political events, weather and other factors that affect the supply chain for products the enterprise sells?
What if CPQ could analyse regulatory activity and infer impacts on pricing, usage, manufacturing processes used or content included within the products your company produces?
How useful would it be to quote a price that’s good for several months without worrying about a hurricane blowing away your key supplier’s factory? How valuable would it be to confidently hold onto a price knowing your competition is months away from matching your offering’s capabilities or advantages?
Mitigating Hiring, Planning and Forecasting Risks
Before demand-centric planning, sales projections were almost entirely based on the revenue trajectory established during previous years. We used to refer to this practice as “steering your car based on what you would see in the rearview mirror.” The obvious drawback to this practice is its failure to take into account the element of what “might happen” in the future.
Hiring projections, budget numbers, IPOs and other forward-looking processes need something besides a record of historical events to effectively mitigate the risks associated with failing to attain anticipated performance levels.
What if companies were able to analyse specific data from disparate sources and derive inferences from that data to guide their decision-making management processes?
Smart Business Applications and Predictive Analytics
Predictive analytics, built into our software tools, frees us from the dependency of driving with the rearview mirror. Increasingly, tools like CPQ, marketing automation, CRM and others are becoming more adept at exploiting predictive analytics to boost their performance levels and values delivered to their users.
Artificial Intelligence (AI), combined with predictive analytics and free-form text analysis, provides inferences and insightful guidance to users.