Originally published in the Nowa Sprzedaż magazine in the May/June 2018 issue.
Working with a client, I see that where analytics begins, opinions end. I would like to address my article to sales managers who want to relieve themselves from opinionated bosses and are tired of the excuses made by marketers. They often say: “You got the lead, so why aren’t you selling?” However, if you don’t have a boss with negative opinions or a marketer who makes lots of excuses in your company, don’t turn the page – you can still use the knowledge from this article to traditionally increase revenues and profits worked out by your teams.
Let’s assume that you only have the necessary metrics to run the business. I know from experience that even customers who misjudge their analytical skills have data on at least a few things. These include among others:
- Total revenues in a given month / quarter / year.
- Total number of transactions and invoices along with amounts.
- Payment details (whether they are coming, or how long are the delays if they ever happen).
Sometimes clients must get this data from accountants or administration. This information is measured by individual sellers and, if there are more of them, also by teams. This knowledge is extremely important – it allows us to pay commissions to salespeople and sales managers, as well as to run business finances from the sales side. However, it isn’t enough to manage sales or to translate marketing activities into sales. In my article, I will focus only on sales and new business sales reports. I will skip the information about post-sale or marketing service.
Who shouldn’t measure anything more?
Despite appearances, there is a group of companies that won’t achieve too much by measuring more than the above-mentioned parameters. If you aren’t interacting with more than a hundred potential customers annually, you can easily opt out of analytics to work on increasing the scale of operations and the quality of individual elements of the sales process.
The only thing to pay attention to in analytics is to make sure that data is constantly collected. Later, when you increase the scale of your business by gaining more clients, it will turn out that it’s a very valuable source of information. I know companies that have so many large transactions that it makes no sense to work on quantitative data. This is true even when they have a business that exceeds tens of millions of zlotys in revenues. My guess is that the vast majority of you don’t belong to this category.
Where to start?
The best step to start with is mapping the individual stages of the sales process and the level of conversion from one stage to another. This means that we should know how many leads we have on a monthly scale (customer queries, recommendations or responses to cold mailing, etc.). Then it’s worth analyzing how many of these meetings end with a summary. The third step may be, e.g., contracts sent to the client, and the fourth step – signing contracts.
Sometimes, of course, there are more stages. We can deal, for example, with the stage at which valuation is sent to the client, in the case when each offer is priced separately. The key, however, is that we only measure what leaves a mark behind. What do I mean by that? We shouldn’t treat such things as, e.g. verbal consent of the customer for purchase or attempts to contact them as separate steps. When we know how many meetings, contracts, and transactions we get from 100 leads, we can aspire to gain predictability and repeatability in our sales processes. Without this knowledge it’s impossible.
Once we have this information, we are able to put another layer over it: the length of the sales cycle. In other words, we need to know how much time it takes customers to go through our entire sales process and how much time they spend at each stage. Next, we want to get the mean and median out of it, so that we know after what time we can expect a transaction after generating 100 leads, as I mentioned earlier. We can also start optimizing the individual stages. Ideally, customers should go through the whole process faster.
If we already have the first two steps behind us, it’s worth focusing on lost transactions. We should know how many transactions we are losing at each of the above-identified stages, and most importantly – why. It’s essential that we try to identify the types of losses we can influence and actively counteract them where they occur.
All of these values should be measured in separate funnels for different lead sources. Usually, the leads that come with recommendations will behave differently than the leads that come from cold mailing. An example of such difference in behavior is that usually website transactions and those based on recommendations have shorter sales cycles than those from cold mailing which, on the other hand, often have a higher average transaction level. Once you determine which marketing activities your transactions come from, present the results of these measurements to the marketing team. Also, suggest that they increase their efforts in those channels that give you the most and / or the largest transactions. At the same time, you can suggest reducing their efforts on activities that don’t generate leads. Without breaking down your metrix lead sources, you will not be able to apply the tactics from the next paragraph; hence, do not omit this in planning your sales records. If you run a business on a scale larger than just one country, it’s worth to also analyze which countries our leads are coming from– deviations in behavior between countries can be as significant as between the sources of leads.
Conversions from stage to stage, the length of the sales cycle, and the reasons for lost deals, broken down into stages, should be compared with each other and compared between sellers. I assure you that when you conduct this analysis diligently, it turns out that your sellers have different average sales cycle lengths, average transaction values, conversions from stage to stage, and so on. Sometimes these differences are small, but other times they will be significant. Where they will be large, use the technique that I call optimizing for the local maximum. As part of this technique, you just need to identify sellers who have the highest positive rate in the team (e.g., those who have the highest level of an average transaction or an extremely short sales cycle) and ask them to train the team in the ways they achieve it. This way, those who perform worse at the given stage should start emulating the best practices.
Drawing conclusions
The last metric among the basic ones are the activities. Do you want to know the number of phone calls people make, meetings they hold and e-mails they send? Hard work usually correlates with results. Where excellent results appear without diligence, it’s worth being attentive. Perhaps we are dealing with a seller who works hard, but doesn’t register it in CRM (Customer Relationship Management software). However, what if we are lucky and dealing with a seller who can manage their work in such a way that they sell a lot without having to spend long hours at work? In this case, you should closely analyse their ways, because probably the team will be able to learn a lot from that person.
A frequent mistake of inexperienced analysts is looking at numbers without a wider context for why they occur. Based on numbers, you’ll probably all realize that the best type of leads are recommendations. However, in the vast majority of businesses, your ability to generate more high-quality recommendations differently than by working on the product and customer service is limited. So remember to draw such conclusions from your numbers which can be translated into action that in turn can bring effect. Another often repeated example of ignoring the context is using a salesman with the longes tenure as the model example. Usually, they’re not a worthy role model, they only have the highest number of clients being referred to them, hence they look like a super-seller. In fact, their numbers may simply be the result of years of work of the entire company team on customer satisfaction.
