Our first blog in this series on artificial intelligence and sales discussed the reality versus fiction of AI, and its real role in business. Now let’s have a look at a factor that must be in place for AI to exist at all: big data.
Big data could be defined as “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.” I predict–along with many others–that the next decade will witness a sea change in the way large and midsize businesses manage their sales functions. Companies that fail to adopt new realities and adopt new practices risk falling behind their competitors who do. To succeed, your company must learn to harness the power of data analytics. In other words, you need to completely understand all that data that’s coming your way.
Big data is rather like an iceberg–most of it is not visible and has to be revealed in such a way that it can be grasped. We could say that the Titanic sank not only because they saw that iceberg too late, but mainly because most of the iceberg could not be seen.
Data and Quota Attainment
In poll after poll of sales teams today, a top complaint is that sales reps routinely miss their quotas. It can easily be seen that companies are not interpreting data correctly, or are missing vital pieces of data–otherwise these quotas would be attained, at least most of the time. It seems that sales is often operating in the dark.
Unfortunately this failing usually leads to a “blame game.” Sales says, “Marketing is providing us with terrible leads!” to which Marketing replies, “Sales just isn’t closing the leads we give them!” But this routine never leads anywhere. The real answer is the right data.
A recent report showed that 71% of salespeople are only moderate performers. 20% are high performers, and 9% are actually underperformers. Why are more salespeople not performing? A good part of the reason is understanding–of buyers, prospects, and markets. Only with understanding can come results.
Technology and Big Data
We are in the midst of what many call call the 4th Industrial Revolution–the digital revolution. In this digital revolution, technology is the driving force. According to Pierre Nanterme, Chairman and CEO of Accenture, “Digital is the main reason just over half of the companies on the Fortune 500 have disappeared since the year 2000.”
This revolution will change everything. People sometimes don’t believe it and are incredibly surprised, because it’s all happening faster than comprehension allows. Every single day, more knowledge is uploaded to the Web than was contributed to humankind in all of the last 2,000 years. The question becomes: how can all this data possibly be evaluated? It certainly cannot be efficiently accomplished by humans scrolling through it all. That is where technology comes in.
One technological aspect being applied to data is that of cybernetics–what W. Ross Ashby called “The Science of Simplicity.” With our product Pipeliner CRM, we apply cybernetic principles in order to reduce the great complexities of data to simplicities for understanding. When the science of simplification is not applied, there is too much data to comprehend.
I was recently giving a presentation in Chicago, and one of the attendees was an executive of Red Bull. Red Bull has last year’s data from 6 billion customers who purchased their product. How can anyone possibly comprehend data from 6 billion people? No one can even read it.
Simplification of data is applied in many ways, all around us. For example, you can go to your local bank, insert your ATM card or a credit card, enter your PIN, and access your account. Very simple. But the complexity behind this operation is enormous: they’re checking if the chip is correct, referencing your name, your PIN, your account details, account history, accessing security logs, and many other factors.
Not only must data be simplified for understanding, but it must be done in a timely manner. Otherwise data becomes stale and unusable. This is especially true in sales, where response to data (for example, leads) should be as instantaneous as possible.
Cybernetic principles–the science of simplification of big data–is a primary application of artificial intelligence. Interestingly, just as we cannot understand big data without artificial intelligence, we also would not have artificial intelligence without big data to support it.
Business Aspects of Big Data
The business aspects of big data are basically answers to three very important questions: What has happened? Why has this happened? What could happen?
In answer to the question What has happened? we have data mining, which has been discussed and innovated for over 20 years, and is now at the forefront of big data. It is the exploration of huge data sets for the extraction of data relevant to a particular project, business, or industry.
Data mining is utilized in descriptive analytics, or lagging indicators–those metrics that reflect past performance. What happened with your leads, your opportunities? This is where you look.
Then we want to know Why has this happened? For that we are utilizing diagnostic analytics, statistical relationships in data. For instance, why was that lead hanging in that one stage of the sales process for so long before it was converted? When it became an opportunity, there was another competitor already there. If you have a sales force that is 35,000 strong, you cannot possibly take the time to ask one of them why they were too slow converting a lead.
Lastly we want to know What could happen? For that we use prescriptive analytics. This is the modeling of data, and the utilizing of leading indicators which show us possible futures, the exploration of possibilities.
For example, in What has happened? we found out we have not contacted our last 8 customers. What should we do? Contact our best customers once per month.
I have come up with the motto Instant Dynamic Visualization for our product Pipeliner CRM–and I truly believe that is the kind of technology needed so that data can be properly evaluated and used by human beings.
Human Aspects of Big Data
For humans to adopt big data and AI, there must be a benefit. How would it used? People have to see the benefits for themselves.
People adopt technology that is useful, easy to use and means an instant savings in work. ATM technology saves people from having to wait until a bank is open, go inside the bank, wait in line, fill out forms and talk to a teller. At any time of day or night, they can make a deposit, withdrawal, transfer and more.
When such technology becomes available, people adopt it quite rapidly. In another example, it was a very short time from when smartphones were introduced to the point (now) they are everywhere and people seemingly can’t live without them.
Technology certainly isn’t a magic bullet, and there will always be room for innovation and improvement. There will always be expanding solutions. ATMs, once again, have certainly improved dramatically over the last 30 years.
Because of the human aspects of big data, salespeople need to continue to learn how to respond to it and use it smartly. There are many, many books out now on “Sales IQ” that deal with this and enable salespeople to move forward.
How Much Data Being Actually Used?
Looking into the actual use of big data by companies reveals some very interesting statistics. 55 percent of executives fear that they can’t keep up with the complexity of big data. 52 percent of organizations have to break big data into small pieces just to work with it. In fact, most companies only analyze a mere 12 percent of their data–leaving 83% of it to chance. That means those companies aren’t even working with big data, but small data.
Just ask yourself: If you could possibly know 100 percent about some subject rather than a mere 12 percent, would your judgment about that subject be affected? For example, if you had to make a decision about the care of your health, and you knew the doctor with whom you were consulting was only utilizing 12 percent of the data about your body, how effective do you think that decision would be? It might even mean your life.
The IT Challenge
Companies must be able to unlock as much of their data as possible, to make the most accurate decisions–and this is the challenge before IT.
Great strides are being made, however. The open source repository GitHub has 24 million developers working on 68 million programming projects. This is huge. People may say that it’s no big deal, that Facebook has nearly 2 billion users. But they must bear in mind that, with GitHub, we’re not talking about users, we’re talking about programmers, and the number of programs being developed. Some of this code will be refurbished over and over, and will be made better and better.
In my ebook Accomplishing the Impossible: Lessons from the Apollo Space Program I brought up Margaret Hamilton, a woman whose name was conveniently left out of any historical mention of important people associated with the Apollo programs and lunar landings. Hamilton was the director of the Software Engineering Division of the MIT Instrumentation Laboratory, which developed onboard flight software for the Apollo program. At the time, real computer programming skill was quite rare. Imagine now that in GitHub we have 24 million people with that kind of potential! In just a few years we will see things that we cannot even imagine today, innovated at unbelievable speed.
On the hardware side, computer chips are becoming smaller and smaller, and at the same time are becoming exponentially faster and more powerful. These chips are used everywhere, and are even finding their way into the human body where one day they will be used to store all the data about a person’s health and even life.
Big Data and CRM
So what does this all mean for CRM in the future?
First, we must overcome the majority of false ideas about big data. The subset of big data must be most usable for the single user, and fall under what I call Instant Dynamic Visualization.
This same principle takes us right into the fact that useable data has to empower salespeople, so they can act rapidly, not react. We must be bold and firm in prioritizing of opportunities, to focus on the capabilities of future growth.
The bottom line: Everything in artificial intelligence is based on data. If we don’t fully work out how to deal with big data, we have no AI; it’s not even possible. So fully analyzing and working with big data is the key to artificial intelligence.
Pipeliner CRM empowers salespeople by visually simplifying complexities. Download a free trial now.