You have received a large multi tower RFP. You started digging into proposals whether you had responded to such RFPs in the past. You wanted to orchestrate this RFP based on your organization win-trajectory, the past history of how you won and lost such deals.
But there are no such insights – What was the outcome, what are your dominant sweet spots, who is your most dominant competitor for a region, which engagement models are you most successful, how are you positioning your company and offerings, which geographies are you doing good, what solutioning or proposal making constructs does your organization create differentiation, what value propositions are you crafting the best, which power centers or stakeholders are you rallying behind, appropriate win-loss pattern etc.
Does there exist such intelligence, inferences, or any analytical insights? In other words, do you have RFPs/Deal/Pursuits Prescriptive Analytics, a guide to win more such RFPs.
You also wanted to know for which of the above aspects your company was losing RFPs as a pattern, if you are careful and avoid those mistakes, your chances of win may be higher.
Every company into competitive bidding or into proactive business development, should have some kind of Prescriptive Analytics – it is pretty simple to develop one and you may not be aware that you already have half of it ready. Almost all companies which has some sort of process responding to RFPs or bids have enough data to infer and derive Prescriptive analytics. You read it right, almost all companies have it.
Having made such a sweeping claim let me walk you through my point of view.
I will use three major sections to drive my point:
1. Elevator pitch on Business Analytics – Descriptive Analytics, Predictive Analytics and Prescriptive Analytics
2. Your organization Large and Strategic Deal shaping process, and finally
3. Creating Prescriptive Analytical insights from your own proposals data
PART 1: “Analytics” is the systematic computational analysis of data or statistics.
Business analytics (BA) is the practice of methodical exploration of an organization’s data, used by companies for decision-making. The three major parts of this analytics are Descriptive analytics Predictive analytics and Prescriptive analytics.
Descriptive analytics looks at “past performance or outcome of your already submitted proposals” to understand the reasons behind past win and loss.
Predictive analytics is where we find out “what is likely to happen” for prospective proposals to determine the probable future outcome of a win or a loss.
Prescriptive analytics is “anticipating what will happen and when it will happen, but also why it will happen.” It is the precautions we take not to repeat past mistakes of losing again. Further, prescriptive analytics suggests decision options on how to take advantage (winning a large RFP) of a future opportunity or mitigate a future risk (not losing a large deal or existing customer) and shows the implication of each decision option. Prescriptive analytics can continually take in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options.
We all know Analytics is best derived from both structured (Database stored data) and unstructured (emails, social media posts, web sites etc) data, using a combination of advanced analytic techniques and disciplines to predict, prescribe, and adapt.
PART 2. Your organization RFP Proposal shaping process
Every company both into competitive bidding or into proactive selling, will have some kind of a process to respond to RFPs or create proactive proposals. Matured and process-oriented companies usually will follow the below process steps:
1. Customer Profiling – gathering intelligence on customer market, business, competitors, products/services and geographic details. Customer’s customers/consumers and RFP G&O analysis.
2. Customer Needs Analysis ensuring most implicit needs besides explicit ones, most dominant desires of across stakeholders, users and mainly its customers.
3. Team to present and defend your solution/proposal, a combination of functional, technical, domain and sales.
4. Outsourced v/s in-sourced evaluation, stakeholders and their stakes, combined with evaluation process and parameters.
5. Customer side stakeholders and their stakes –who is impacted by your solution or proposal.
6. Inputs from Third-Party Advisers (TPA) for relevant advice to ensure a win.
7. Right positioning of your solution, company and offerings, that you are a strategic partner than an operational vendor. Most compelling Win Themes for each of the customer’s dominant desires or hot buttons.
8. Identified various risk parameters in detail and mitigated those risks. Developed hard hitting Value Proposition, value realization plan, Benefits Value Quantification, tangible and intangible aspects
9. Most appropriate engagement model – BoT, aBoT, Hybrid, Captive, Virtual Captive, ODC etc
10. A perfect BAFO (Best and final offer) to your product/service, buy back services, YoY price increases, warranty, service assurance, SLOs and BLOs
11. Relevant Customer references, case studies and testimonials most relevant to the RFP, customers G&Os and implicit needs
12. Identified competitors, done detailed SWOT analysis, leverage the “Boasting” and “Ghosting” technique to undermine the competition and their offerings.
13. Series of pre-submission proposal/solution reviews – red team review, green team review etc.
Your proposal has gone thru the above process of solutioning, the complete nine yards, with all the win ingredients. You have stored this huge data in one or the other digital form. Post submission of proposal, you also have detailed win loss analysis. Your proposal is nothing short of a huge sales novel by now. This is your gold mine of data you can leverage for Prescriptive Analytics.
PART 3: Creating Prescriptive Analytical insights from your own proposals data.
Next time when you get an RFP or proactively pursuing an opportunity, pull insights from this gold mine. From this existing data, you can derive greater insights, competitive details, win inhibitors, win loss trajectories, positioning etc. Use this data to get prescription of the past, create your own rules and algorithms to determine past trajectory, analyse how you won, incorporate that, analyse where you lost and eliminate those.
An illustration to justify the above: You have received a large RFP from a P&C Insurance company from Texas State, US. RFP has all relevant details. With this little detail, and leveraging your gold mine of data, get prescriptions on the following:
1. Top prescriptions of winning deals in P&C Insurance domain
2. Precautions to be applied for the state of Texas and even for US as a Geography for Insurance RFPs
3. Must be aware of factors against major competitors in the past, % of win and loss against each competitor. If it is company X against whom you are consistently losing, what should you avoid during the next RFP
4. Engagement models which has failed in the past for P&C Insurance and for Texas state
5. Contracting or negotiation aspects for P&C Insurance which you have lost for and how to avoid in future
6. The list can go on. You can analyse yourself when, where, how, against whom, for what stakeholders or power centres, at what price, which engagement model, win themes and customer dominant needs do you win or lose. Create a tool or system or process such that it prescribes you where you did good and where you did not. Design it in such a way that it prescribes you what to do next time when there is a particular competition or need or engagement model or need for a service or product.
Study, assess, infer, apply algorithms to this data and you will have your own Predictive Analytics. Accumulating these small set of efforts and creating insights for a large number of pursuits can create a larger gold mine. By doing this repeatedly you have kind of gearing towards Artificial Intelligence.
I am not aware of any CRM companies which has ready to use algorithms for immediate consumption. But I know a few niche product companies having near similar product and algorithms which can be customized to create Prescriptive Analytics. I can be of help in mutually connecting each other.
This article originally appeared on pursuitmentors.com