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  • Yvette Pearson

Sales Forecasting and Conversion Rates

What on earth is a Conversion Rate and why would you need one?


In simple terms, a conversion rate is the number of deals that you close, as a percentage of the ones you look at. How you define "close" and "look at" can vary wildly from person to person, and company to company.

It helps you to forecast how many deals you might close in the current financial period. Conversion rates have been the subject of much debate throughout my career so I wanted to share my advice on how to build a really robust forecasting system.


When you are building these sales forecasts, a data person will say to look at the historical conversion rates and apply that to your current pipeline.

A sales person will tell you (probably in colourful language) that it's much more complex than that, and each deal/month/salesperson should be looked at on their own merit.

I say - it's a combination of many different factors, including these.


Considerations


Firstly, conversion rates fluctuate. A lot. Not only should you be taking into consideration the time of year and volume of data that you have, but there will be anomalies in all data. Economic factors can play a huge part. You might have a random elephant deal which skews your data one year. You might have discontinued a product, or launched a new one. Perhaps you have just hired a load more sales people and want to forecast what they can win.


Secondly, over what time duration are you forecasting?

Most of the time it will be likely that you will be forecasting for three time periods:

  1. Present day to the end of the current financial year, generally broken down by month - "are we going to meet our target this year?"

  2. Next 12 months, generally broken down by month - "how do we plan for next year?"

  3. Next 2-5 years, generally broken down by quarter - "what is our long term strategy?"

If you compare this to the volume of data you have collected, you might not be comparing apples with apples.

For example, if you have six months' of data but are forecasting for the next couple of years, there are assumptions (like summer/winter/holidays) that you will need to provision for. Should you expect a slow-down over summer? Are your salespeople due annual leave? Does your data cover the Christmas and New Year period?


Thirdly, consider the length of your sales cycle: the time it takes from qualified lead to close. If it's longer than the time period of your forecast calculations, there will be leads which are currently unqualified which could close within that time period.


This is actually a really important point when forecasting and I'll use an exaggerated example to show you what I mean.


Imagine your sales cycle is 9 months from lead qualification to close. If you are calculating your forecast in the middle of the financial year, chances are there will be no new leads coming in which will close that year. Therefore ALL of your deals for that year need to already be qualified. If it's looking like you are going to miss your annual target at this point, no amount of marketing is going to help you.

Therefore, any amount of additional sales or marketing work you are doing now won't come to fruition until the next financial year. So if your sales team is underperforming, no amount of encouragement or putting them onto performance plans is going to save you.


Finally, how are you defining the terms in your forecast? I already mentioned Qualified Lead. How is a lead Qualified? I bet if you ask your sales team and your marketing team this question you will get wildly different answers!

I worked at one company where the marketing team defined their leads as "anyone who has visited the website more than once". They were handing off hundreds of "qualified leads" to the sales team who were doing nothing with them because "all those leads are rubbish".


It's SO important to make sure everyone has a definition of what is a Qualified Lead. You can even split them into Marketing Qualified and Sales Qualified, and I will take you through a little later how these two types can feed into the same forecast.



So where should you start?

In order to address the considerations we just discussed, you should always start with the data. Without data, you are just another person with an opinion (one of my favourite quotes!). If you are not collecting sales (and loss) data now, you should start. Gathering it all on a spreadsheet is fine, but I'd recommend using a proper CRM system. Free versions of systems like Hubspot are absolutely fine while you are starting out. This is important as soon as you start building dashboards and things so you'll thank yourself later if you start this now.


Ideally you'll have several years' worth of data, but any more than a couple of months is good to make a start.


What Data should you Collect?


Generally speaking, I'd always say you should be collecting as much as possible. If you're not sure where you begin, I've created a list of some standard data you should be able to start collecting easily.

I say should because once people know to write things down, they'll do it. But actually getting them to do it in the first place is often a challenge. This is where a CRM system can help as you can make certain fields mandatory.



You've got the data. Now what?

This is where you can make a start on the data-only conversion rate. If you've been collecting the minimum amount of data I mentioned in the standard list, you can easily calculate some basic things.



Conversion Rate

Now you know how many deals you looked at, and how many you won, divide one by the other and you have your basic conversion rate. Assuming you are counting a lead from the point of initial contact, I'd expect this conversion rate to be somewhere in the region of 5-25%.

Of course, you can look at this on a £ or $ level, but also looking at the number of deals as well.


How long does it take to close a deal?

Remember, "close" doesn't always have to equal "won". Closed can mean that you had confirmation from a potential customer that they don't want to go ahead. You can look at the date you first had contact with the customer, and the date that the deal was closed. Subtract one from the other and you have the average time it takes to close. Here you can split them out between Won and Lost.


With these to calculations, you can look at your current pipeline and draw some quite useful conclusions.

Using your total pipeline and your historical conversion rate, you can calculate how much of your pipeline you could reasonably predict will close, and over what time period.


Congratulations! You've just created a forecast!


Next Steps

Of course, if you present that basic forecast to a sales team, the chances of them being happy with it are virtually zero. You now need to consider the other variables and one-offs. I'm talking about things like elephant deals which might be skewing your sales figures or pipeline, and the fact that not all deals will take your average amount of time to close.


One thing you could do is remove the top three or four highest value deals, and the bottom three or four. This can remove the anomalies and help flatten your data a little.

You can also make a provision for, say, one elephant deal per year on top of your forecast. How you define this large deal is up to you, but generally it will be something so large (or so small) that your entire dataset will be messed up if you include it.

You can also consider giving your Time to Close calculation a range rather than a specific number. For example, an average deal might take between six and nine months to close. This is definitely going to be better received by your sales colleagues, and is probably more accurate.


Next you need to consider - as we touched on earlier - the cyclicality of your business. Do you get half of your annual sales in Q4? If so, there's no point in dividing your annual forecast by 12. What I've done in the past is split it into quarters, and apply a weighting to each one. Reducing by half over the summer, and increasing by half over the autumn. The objective here is to help your friends in finance plan for bumps in sales. It doesn't need to be exact.


Outcome

Now you've done all of this work - and it's probably taken you many months - you can update your forecasts on a regular basis as you get more and more sales data.

You will have a conversion rate for your pipeline, and a timeframe for realising those sales. You will have a weighted forecast by months or quarters to take into account the seasonality, and you will be able to say with confidence that you can expect one or two large deals on top of this forecast.



Taking it one further step, you can now work backwards with your conversion rate and sales targets to see how much business you need in your pipeline in order to meet your targets.


For example, if your conversion rate is 20% and you have a target of £1m per year in sales, you know that you need pipeline of £5m in order to have any chance of reaching this target. THIS is a really powerful metric for your sales team and you'll have all the data to support it.



Summary

Sales forecasts and conversion rates are incredibly complex and emotive. By including as much data as you can, being flexible, applying common sense, and continuously updating them, you can build something that continuously improves which is an accurate reflection of your business.


I have worked with many sales teams throughout my career and built simple and complex sales forecasts. I can help your organisation make sense of the data you have, and build conversion rates and forecasts that are appropriate for your business. Get in touch to see how I can support you.

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