Data instead of cold calling – precision in sales thanks to technology

Due to the high complexity in the sales process, one was always dependent on coincidences or luck in sales. Every person is different, thinks differently, reacts differently, has different interests, etc. Therefore, it always takes a large number of potential prospects to get into conversation with and many more to see our advertising before we get an inquiry for our product. Acquiring new customers has therefore never been a popular task, because by nature the majority of people we come into contact with will have no interest in our product. It doesn’t matter what the product is. It always took a large number of conversations and contacts before the first results became apparent. The result is an everyday life characterized by rejection and frustration. Today, however, there are more precise methods for acquiring new customers.

How cold calling used to work

“Mass brings class” has long been a tried-and-tested rule in sales, which is becoming increasingly difficult to implement in the digital age. If it can still be implemented, then at ever increasing costs. In traditional sales, the focus was often on obtaining the largest possible number of addresses or leads in some form or another, which were then contacted. This involved contacting the largest possible number of people who may or may not have been interested in the company’s product. A note in advance: most of them were not.

This approach is therefore very tedious for sales people because one is confronted with a considerable amount of rejection. The random people called were rarely pleased to be contacted by the salesperson, and the entire process was cumbersome and frustrating for both parties. Why then did or does companies continue to use this approach anyway?

Because from the large number of many, single qualitative low addresses, nevertheless, some orders resulted gradually again and again.

However, the rates were often miserable, with one customer out of 40 contacts or even less. This was mainly due to the fact that the potential customers were not pre-selected and were not qualified in any specific way. It was the classic “shot in the dark approach.”

“Shot in the Dark Approach”

Imagine you are in a completely dark room with a rifle and you have to shoot at a target. What do you do? You can just try to aim in any direction on the off chance and pull the trigger. Maybe a bullet will actually stray toward the target. If you have enough cartridges, you can actually hit the target with it sooner or later. At least with a few tries. However, in many cases you will run out of ammunition before you hit the target.

Cold calling is similar.

Only instead of cartridges, we use leads. If there are enough of them, sooner or later one or the other will actually be interested in our products and also buy. In a room of 100 people, there will always be one person who just wants to install a new kitchen, one person who just needs a new car, one person who just needs new shoes, and so on. For every product, no matter how unprofessionally you would sell it, you will find a customer after some time – if you just keep approaching new people about it long enough. This random principle works, but it wastes a lot of active sales people’s time. By making this process more effective and using our team’s time more wisely, we can achieve a sustainable increase in productivity.

In many cases, however, addresses will be used up before this can result in an actual usable customer base. Especially in the long run, this does not offer a sustainable solution for constant business growth and revenue increases. In times before the Internet, this was the most common method of finding new customers, despite all the disadvantages. Digitalization has changed this, of course.

What does successful cold calling look like today?

Today, companies no longer have to work according to the “shot in the dark approach”. Those who still do this have only themselves to blame. Instead, they work with an ideal customer profile, based mostly on data from previous sales. They look for commonalities that profitable customer groups in particular have in common, and try to multiply the previous customer base based on this pattern. Through digital tools such as digital marketing, ERP or CRM systems, we can filter out this data with a few clicks.

This allows us to guess which companies or people are most likely to be suitable for our product or service and where the probability of closing a deal will be highest. If we now proceed according to this pattern and then search specifically for other companies or people who match this pattern, the probability of meeting a potential customer increases many times over.

These patterns of behavior or characteristics can have different parameters depending on the company and industry. The most common are company size, industry, product variety or similar. In any company, a handful of commonalities can be derived from existing customers that unites them or at least provides insight into their behavior and interests. If this is not the case, you can also try to take a closer look at only the best customers and look for commonalities there – after all, you particularly want to have more of this customer group.

Digital tools can help

Digital tools make it possible to search for comparable companies on the basis of existing customer samples and in turn to contact them specifically in order to market our products to them. For success in modern, digital sales, it is of paramount importance to no longer use unfiltered databases or lists to acquire customers. Instead, we use customer profiles and digital marketing tools to target exactly the right person at the right time. Technology of various kinds lets you search for specific companies or customer groups more precisely than ever before. Numerous search engines and digital business directories reveal pages and pages of new companies or people who might fit the profile in just a few clicks.

Digital marketing tools such as social media or Google provide assistance by creating customer profiles of each of their users and offering predictions for buying behavior and interests based on algorithms. The interesting thing is that these predictions for our behavior have become extremely accurate. They range from personal details of expectant mothers, to people who are about to build a home, to companies that are currently on a growth trajectory. Depending on which group of people or companies is best suited to our product, we can target them and turn them into leads with advertising messages.

In the B2C sector, too, customer groups can now be targeted more precisely than ever before by means of precise advertisements that are delivered only to very specific customer groups with predefined criteria. Here, too, this largely accelerates customer acquisition compared to traditional media such as radio or TV advertising, where the spread of the target group is very broad. These “old” marketing platforms, such as radio or TV advertising, function like cold calling on the random principle, in that an attempt is made to reach as large a number of consumers as possible in the hope that a few from the right target group will subsequently be included. In both the B2B and B2C markets, there are now much more efficient methods of approaching new customers and expanding the existing customer base.

Data instead of randomness in cold calling

In recent years, changes in sales have become noticeable as a result of this advancing technology, which is constantly evolving and having a lasting impact on the day-to-day nature of sales processes. Much tedious work has been automated as a result or is now easier to implement in everyday life.

Innovative pioneers of this trend such as Salesforce.com, for example, have completely converted their sales operations to digitally supported processes. This eliminates much of the previous guesswork and uncertainty of customer contact sales activities and delivers a high return on investment for sales strategies with unprecedented precision. Where previous methods in sales often worked at random, digital tools now allow almost surgical precision in the allocation of resources and distribution of tasks. This precision in approach, compared to analog sales, nets out from the collected data that emerges from the entire sales process, including, for example:

• Which products are more in demand in overseas markets than in ours?
• Which version of the product do we offer best in which market?
• How many customers respond to our new promotion?
• Which add-ons or equipment variants are hardly sold anyway and thus only increase complexity unnecessarily?
• Which of our e-mail mailings have a low click-through rate?

These details and many more allow the entire process to be systematically examined for its strengths and weaknesses. Anyone who wants to digitize their sales today, or at least use digital tools, now has the opportunity to get a picture of where improvements are still needed, where there is potential for better performance, and which areas or processes should perhaps be rethought by taking precise measurements of the data. The insights gained from this allow the sales process to be optimized down to the smallest detail. All this data and much more can be generated via digital marketing, digital tools such as CRM systems and platforms for sales. Based on this, fact-based decisions can then be made by sales and management to improve the overall performance of the company.

What data should we measure?

The decisive factor for implementation is also to use the data effectively and not just to generate a vast amount of data out of enthusiasm for the new technology. After all, it is easy and fast for digital processes to accumulate inconceivable mountains of data in a short period of time. These quickly generate a workload that is no longer manageable for the employees responsible. Gathering insights from this data or reformulating it into concrete instructions for action also becomes increasingly difficult as a result. It is not expedient to let digital systems automatically collect all conceivable data without a concrete purpose for doing so. When we generate data, it is important to analyze beforehand exactly for what purpose we are collecting this data.

As with any sensibly managed project, we should have a goal with our digitization, but at the same time remain flexible enough to make course corrections if necessary along the path that will lead us to our goal. Within this project, we need to understand what data is actually relevant for us to achieve this goal. Within this data, for example, existing processes or material lists, customer behavior or employee completion rates can be analyzed. Each of these figures again provides different insights and allows us to make informed decisions in different situations.

We can call the numbers that are important for our project and the relevant area key performance indicators (KPI). Every area in the company has certain key performance indicators that can be monitored. Every activity, every role generates a certain output in everyday life. This can be mapped and measured using figures. If we now set targeted measures to better achieve internal goals, the KPIs of the individual areas can be monitored. This enables us to say precisely whether the measures taken are achieving their goal or not. In the event of a change of any kind in as yet unknown territory, we have thus built a control mechanism into the process that allows us to always check what the current situation is. Especially when we incorporate new technology into our sales process, we can use properly set KPIs and the digitally generated data to generate precise courses of action for our sales team.

But beware: some changes only yield their results with a time delay. Therefore, do not immediately stop a project if it does not immediately bring improvements to all figures. If nothing improves in the long term, it is time to look for alternative solutions in a further experiment or to fundamentally question the chosen path. Once a new solution has been found, we repeat the process and test the results again until finally, sooner or later, a method emerges that enables us to achieve our goal.

Selection of the right tools in sales

In order to be able to realize this process, companies that put it into practice must select the individual programs, platforms and tools with which the data is subsequently to be collected even before the digital transformation in sales begins. To do this, it is necessary to select the available tools and sales models through documented learning processes, similar to those in a start-up. For implementation, we initiate targeted experiments to make a validated decision based on data and measurable results. However, due to the unpredictable nature of events and the unpredictable behavior of humans in the sales process, victory or defeat can often be close in reality.

Failure of the project does not mean that you were necessarily wrong in your assumptions and made a mistake in your planning. However, success in reverse does not mean that one was highly intelligent and superior with one’s assumptions. Rather, individual events are simply unpredictable in nature and must be understood as a calculable risk – which ultimately results in an experiment. After all, all new technologies that we want to test for our company and implement in sales are first and foremost just that, experiments with an uncertain outcome.

Digital sales offer much better exploitation and optimization of customer potential, for example through data-driven cross-selling or upselling, as illustrated by the example of Amazon: “Customers who bought this item also bought:”.

These sales tactics rely on a precise collection of data that a customer leaves in their own system. Every second scrolled and every mouse movement is evaluated and in turn permanently changes the next shopping experience. The system thus learns about its users and their behavior in order to personalize the individual’s experience from this data. Each customer thus has their own personal shopping experience on Amazon or their own personal search results on Google. A company can use all of this to better deliver exactly those products and content to customers that are also relevant to them.

Every customer who comes into contact with our company and whose digital footprint can be analyzed offers an opportunity in this way to offer further products and services. These opportunities open up completely new ways to offer more products and expand the existing customer base. All of this is done in a targeted manner based on the interests of the customer and their previous behavior on the respective platform. You thereby take the random principle and the necessary big numbers out of traditional sales and marketing activities and are no longer dependent on luck. The resulting product recommendations have the highest possible relevance for each customer personally and in turn increase the probability of a purchase.

Effective use of digital tools and the resulting data creates cost advantages compared to traditional field sales, which can be used to specifically increase a company’s sales and profits. In addition, this treasure trove of data allows decisions to be made in a much more informed manner. You no longer have to rely on gut feeling, but can measure which products are well received where and how.

Above all, because these opportunities would never have come about in a traditional sales environment. After all, this treasure trove of data was not even available to companies until a few years ago

David Schneider

David A. Schneider hat über 10 Jahre an vorderster Front im Vertrieb und im direkten Marketing verbracht. Sein erstes Unternehmen hat er mit 18 Jahren gegründet und war seither von den Möglichkeiten und Auswirkungen des Unternehmertums auf unsere Gesellschaft fasziniert. Seit Jahren hat er es sich zur Aufgabe gemacht, ein Buch pro Woche zu lesen und die Inhalte in der Praxis anzuwenden. In diversen Branchen hat er damit bereits überdurchschnittliche Ergebnisse erzielt, indem er seine Arbeitsweisen stets an die neuesten Technologien anpasst und orientiert. Derzeit hat er eine leitende Funktion in einem Familienunternehmen mit 150 Mitarbeitern und teilt sein Wissen als Autor, Blogger und Unternehmensberater mit seinen Kunden und Lesern.