Continuous learning of buying criteria to improve Win Rate
Hello,
Here's a proposal how we can implement "continuous learning" model in BNI, which has a potential to even double the Member Generated Revenue. With marginal investment, simple model and yet very high added value to members who try to give high quality referrals and who try to have high Win Rate in closing the ones they get from other members.
Model is similar to Amazon in B2C e-commerce, from which many of us have ordered a book. Machine learns from previous buyers behavior and the tool proposes you another book with high Win Rate. Similar capability can be implemented in B2B-business and especially BNI-type of businesses have high potential, due to the strict process discipline and by nature a close link to buying behavior (reference is often among top 3 from the total of 19 buying criteria).
In order to improve Win Rate of referrals, we would need a few small improvements in the Connect tool and the process. In short, the following Connect-tool related requirements are:
1) Now we only register if the lead was won or lost. We would need understand the reasons for winning/losing (why did we win or lose??). This would require field with pull-down menu of 19 buying criteria (one is references, but also many others such as brand, price, quality, location, trust, financing, ecology, price,..). For high quality data analysis, it would be best to have a model where we select 3 most important reasons for winning/losing. This raw data is pure gold, but model needs to be systemic and simple.
2) Not only we want to have quantitative data, but also qualitative data. One open text field would be needed for "learnings". After lead is won/lost, we enter one learning to the system. Furthermore, this learning can be used in next similar cases as best practice sharing (within chapter and even globally) and avoiding mistakes are repeated. Imagine what we can do if we get annually 12 million learnings and you can easily find the 30 which are relevant for closing your case. Or BNI management can easily capture most common learnings for closing deals and implement training for all of us.
3) Heat-levels are not clearly linked with Win Rate. It would be best if we link them e.g. 1=20%, 2=40%, 3=60%, 4=80% and 5=100%. This brings a lot better guidance and clarity vs. current. Also it provides us valuable data to analyse what really was the Win Rate of leads. Typically we tend to believe that lead is hot, but in reality it's not. We can measure if we really closed 8 out 10 cases, if all of them would initially have 4 (80%) heat level. Hard and measurable facts on table.
4) Wit Rate should be added as a KPI (measures the process efficiency). Now the Win Rate calculation relevant data is in the system, but it's impossible to see what your Win Rate was (both ways, for given and received referrals). As a member, I can't see that and I've understood that also chapter, region and country BNI management lack this critical insight. Win Rate is the most important KPI of any B2B-business, but most often it's role and impact is not fully understood - i.e. missed opportunity. WinRate KPI combined with BNI process discipline, is a very powerful combo to improve efficiency and outcome of our business system.
5) Usage of modern Business Intelligence and Analytics tools (such as MS Power BI), would uplift data analysis to the next level, such as continuous improvement of strengths/weaknesses for members and power teams, identification of high Win Rate opportunities, deep and data driven understanding of all buying criteria (not only referrals), and much more. However, any implementation would need an API-interface between Connect and PowerBI for selected data. Obviously, data would be anonymous and access rights carefully managed.
Once this basic structure is in place, same mechanism can be used for "winning" new members (why did they join BNI?) and "losing" old members (why did they leave?). Higher WinRate obviously makes BNI more lucrative and member stay if they get more business in return.
Modern data intelligence and data management needs to focus on root causes "why?" and linked with business relevant KPI (Win Rate). With relatively small investment and change in the process, we have potential to even double Member Generated Business. Many good things happen if we do this.
I've implemented similar models to 250 companies from 200 industries, and from self employed to global corporations. So far, I've analysed 80 000+ won/lost offers and the root causes behind. From this background, I've created a model which is simple (few clicks/minutes to enter data) and business relevant / valuable (this data helps me and my colleagues to win new business).
Looking forward to discussion on this topic and I'm more than happy to provide more details on practical implementation. Most likely a limited pilot would make sense prior to global roll-out, but first we would need some level of go-ahead.
Antti Leijala
BNI Finland, Metropoli Online Match
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Official comment
Thank you for these insightful notes!
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This seems to be a nice model for most businesses to get high quality referrals. Let's investigate this more.
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kullanışlı bilgi0 -
Very good proposal! This would be logical next step in the evolution of BNI referral process.
Usefulness of heat levels is questionable in the their present form, but I also wonder what would be their function in this proposed model. If I understood correctly, member who gives the referral would estimate the change of winning beforehand. Why is this relevant information? I agree that win rates should be monitored after the fact.
Also, I think that the most important parts of this proposal have better change of getting implemented, if renewing the heat rate system is not tied to them.
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This is certainly something that should be discussed thoroughly. Heat levels are not used efficiently at the moment and if we could learn what works and what does not, we could create a winning spiral. Thanks Antti!
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In reply to Jouko Jussila, here some elaboration of the heat levels vs. win rate. Having a concrete and measurable link between the heat level and win rate, forces the giver to thing how likely the winning of the deal really is. It also give the receiving party clear indication that there is e.g. 80% likelyhood to close the deal. Better react fast and close the deal.
Once this is measurable (and transparent), the giver starts to learn to be more accurate in win rate estimations. If you for example send 100 referrals with 80% win rate and after some time statistics show that only 40% we really won, you learn to evaluate your referrals and seek for "hot" ones.
Obviously one option is to create another field next to heat level, but this might be confusing? In this case heat level would refer to the "way of working", while win rate would refer to likelyhood to get business. Actually, if both data would be collected, we would easily find the correlation between current heat level and win rate. This also would be very insightful for the development of the practical process of seeking referrals.
"You can't improve if you don't measure"
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Great proposal this would definately increase valuable information from referrals.
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A great proposal for a deeper understanding of winning and losing referrals. Everyone wants more business, so this is really worth thinking about more.1 -
Absolutely worth for investigating this more.
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I strongly agree with this! We need more information behind references. Why you didn't succeed to win it? What was the reason behind it? Why did you win it? Etc. Quantitative information give you the the direction, but in order to grow your business, you will also need more content, i.e. qualitative information.
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thanks for your post, it is very interesting and I hope BNI can further work on your proposal.
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