{"id":261,"date":"2016-06-20T10:01:00","date_gmt":"2016-06-20T09:01:00","guid":{"rendered":""},"modified":"2017-03-10T23:49:16","modified_gmt":"2017-03-10T22:49:16","slug":"a-truth-behind-the-lead-business-a-comprehensive-approach-or-the-onion-approach","status":"publish","type":"post","link":"https:\/\/www.thediar.com\/en\/blog\/a-truth-behind-the-lead-business-a-comprehensive-approach-or-the-onion-approach\/","title":{"rendered":"A truth behind the lead business: a comprehensive approach (or the onion approach)."},"content":{"rendered":"

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We are obsessed with improving our conversion rates while keeping our traffic levels. But, is this the right approach? I would say yes and no, bust mostly no. Let’s see why.<\/i><\/div>\n<\/div>\n
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Lead<\/b> business is a very complex one. And, as we said in our initial post<\/a>, complexity matters<\/b>!<\/div>\n
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Before jumping in to a discussion regarding leads, let me recall a situation we faced time ago when doing consultancy for an e-retailer<\/b> (a very big one, by the way). They were having a wonderful<\/b> situation<\/b>: around 20 million monthly sessions with around 3% session-to-order conversion rate, very high average order value, and pretty good margins. We were able to close a meeting and we offered them the full artillery<\/b>: UX enhancements, fine tuning of the tracking tool continuous A\/B testing, heatmaps, and so on. They told us: no<\/b>. Surprised by this answer we asked: Why not? We were offering them a very good deal: very low fix fee and a high variable based on success (we did that because the site was horrible: we already identified couple of opportunities that could improve the conversion rate). They replied: we don’t have the enough logistics resources to handle the extra amount of orders that would come<\/b>! Ok. Lesson learned.<\/div>\n
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Some years later we came with a similar case. The context: traditional<\/b> insurances<\/b> company (life, car, and home) starting developing a digital presence. They had a horrible website that was generating around 500 leads per week. As soon as we saw it we wanted to offer the same<\/b> service<\/b> we offered years ago: conversion rate optimization, UX enhancement, full deployment of Google Analytics, etc. That is, we used to see the situation as:<\/div>\n
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However, as a side note, a comment from one of our analysts came to the deck. “Wait!” he said. This might not be the right approach. “Let’s recall the e-retailer situation we faced years ago!”. Indeed, let’s take a picture of the current situation<\/b>. Concretely, let’s take the first step towards a comprehensive<\/b> picture<\/b>:<\/div>\n
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– 500 leads per week generated through the website.<\/div>\n
– 2% lead-to-policy conversion rate (which takes place offline, via a phone call). This means 10 policies per week (simple math).<\/div>\n
– To make 500 phone calls a week, they needed 2 full-time resources.<\/div>\n
– The cost for a full-time resource was equivalent to the revenue generated by 4.5 policies. In other words, the “actual<\/b> margin<\/b>” is around one policy.<\/div>\n
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Now our landscape is broader, much broader<\/b>!<\/div>\n
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With some basic enhancements to the website, we could increase leads by, let’s say, 50% (site was really ugly). With more simple maths we say that we could have 750 leads<\/b> per week generated through the website. But now the key point is that the lead-to-policy ratio would not<\/b> necessarily<\/b> change<\/b>! This means that they would just increase the total number of policies up to 15! We always say that we need to ask the right question. At this point the right question to ask is: how<\/b> many<\/b> full-time<\/b> resources do we need to handle 750 phone calls per week? A simple cross-multiplication shows us that we would need 3. This extra resource would cost us another 4.5 policies. In other words, the total<\/b> cost<\/b> for the 3 full-time resources would be 13.5 policies, so the actual<\/b> margin<\/b> would be equivalent to 1.5 policies! Wow! We did a lot of work to improve the website just to raise our margins by half a policy! This at the end means that, with the current set-up, the business does not look scalable<\/b>. Or, in other words, more<\/b> does<\/b> not<\/b> necessarily<\/b> mean<\/b> better<\/b>. Even worse, what would happen if a sudden<\/b> pike<\/b> (seasonal pike, or a pike due to a sudden reduction of the prices, or by a sudden increase on your competitors prices, or by a sudden increase on traffic due, for instance, by an important investment on marketing) occurs? It’s simple: the 3 full-time resources will not be able to handle all phone calls on time<\/b>. In the insurances universe, if you don’t handle a call soon you have high chances to loose it.<\/div>\n
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How do we solve this? Here we will apply the “onion<\/b> strategy<\/b>“. Imagine your processes as an onion, and each one of the sub-processes are a layer of the onion. The basic idea behind this approach is that you should start optimizing processes from the end of the journey to its beginning<\/b>. In this way, as the user flows he will always step towards a process that we already tried to optimize<\/b>. In the case of the insurance company, the first thing we offered them was to understand whether we can improve the lead-to-policy rate<\/b>. Here a new world appeared in front of our eyes: we taught them not to follow all leads<\/b> (we implemented very cool models to prioritize the incoming leads), we taught them to catch the necessary data<\/b> to understand why a lead converted or not into a policy, etc. At the end we were able to improve the lead-to-policy<\/b> conversion rate up to 15%!. At that moment we were able to improve the website in order to bring more leads. And after that we were able to optimize the traffic sources, the prices, and to understand the competition, in order to have under control the total amount of sessions reaching the website. We got then a much broader landscape for the situation:<\/div>\n
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Despite this is a very simple representation of the full process that ranges from the intention to an actual policy, the exercise behind it is very insightful<\/b>. And this is not only about data but about business<\/b>. The full exercise of depicting the concrete steps through which the user flows up to becoming a client can only come from a deep knowledge of the business. Then, and only then, data science and tools appear. As we always say, the key point is to formulate the right questions. Then you find the data to answer them.<\/div>\n
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As a summary always keep in mind:<\/div>\n
– More<\/b> does not necessarily mean better<\/b>.<\/div>\n
– Broad<\/b> your landscape until you have all the steps that can be improved or optimized.<\/div>\n
– Think<\/b> about the consequences the optimization of a step have to the whole process.<\/div>\n
– Follow the onion approach<\/b>: improve the latest steps of the whole process first and then improve backwards.<\/div>\n
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Good optimization!<\/div>\n

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