According to the data analytics firm, SAS, machine learning (ML) is “a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt.” AlphaGo’s ability to generate new data and learn from experience – described in my previous blog – is an example of machine learning. The expression ‘machine learning’ was coined by Arthur Samuel in 1959. He wrote: “[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”
As the SAS definition indicates, algorithms are essential to ML; but what is an algorithm? An algorithm is a set of rules, a sequence of procedures or a formula for solving a problem. We all use algorithms in everyday life. The routine you follow when brushing your teeth is an algorithm; your favorite recipe for baking muffins is an algorithm. These algorithms may change over time in response to feedback. If a particular batch of muffins fails because of too much salt in the mix, the recipe, and therefore the algorithm, is adapted as a result of learning.
Machine learning algorithms also change in response to feedback. ML influences your online experience. Whenever you do a web search, the results that are served to you will depend not only on the keywords
Over the next few weeks we are going to have look into Artificial Intelligence (AI) and what it means for CRM, and its applications in sales, marketing and customer service. We’ll examine what is already happening and make a few brash forecasts about AI’s future. So, bookmark the page and come back.
In this blog, you’ll find some background on AI – where it all started, and how it touches your life right now. And we’ll hint at what is to come. In the next blog, we’ll explore the conditions that underpin the rapidly growing corporate interest in AI.
What is Artificial Intelligence?
Artificial intelligence is the ability of computers or computer-controlled robots to undertake tasks normally performed by humans. Human intelligence allows us to reason, evaluate, analyse and interpret information, synthesise evidence from various domains of knowledge, make decisions, memorise, predict outcomes and learn from experience. When these tasks are performed by computers or computer-controlled robots, that is AI.
How it all began.
Computers have long been programmed to perform quite complex tasks. If you’re in your mid-20’s or older you may remember 1996. It was the year that Atlanta hosted the summer Olympics, Charles and Diana got divorced, and Dolly the sheep was successfully cloned. It was also the year in which IBM’s supercomputer Deep Blue beat Russian world chess champion Garry Kasparov. Kasparov and Deep Blue were rematched
Some research from the Meta Group suggests CRM can be a mixed blessing. “Business customers want to be identified for their appropriate requirements (e.g., resupply of goods and services that they already purchase), so that they can save time. Many consumers fall into a similar camp. But in exchange for being identified (e.g., providing information about themselves, or having it collected), customers/consumers expect to be treated as “special.” This means free products, better service, useful information, and so on. They also do not want to be bothered by endless phone calls or e-mails to sell them more ‘stuff.’” As customers surrender data about themselves to suppliers, they expect that information to be used wisely to communicate relevant and timely offers.
Spare a thought for poor old for Michael O’Leary, Chief Executive of Ryanair.
After arriving in a hotel in Dublin, he went to the bar and asked for a pint of Guinness.
The barman nodded and said, “That will be €1 please, Mr. O’Leary.”
Somewhat taken aback, O’Leary replied, “That’s very cheap,” and handed over his money.
“Well, we do try to stay ahead of the competition”, said the barman. “And we are serving free pints every Wednesday from 6pm until 8pm. We have the cheapest beer in Ireland”.
“That is remarkable value”, Michael comments.
Adam Smith, the 18th century father of economics didn’t write about value-in-experience. Rather, he wrote: “The word value, it is to be observed, has two different meanings, and sometimes expresses the utility of some particular object, and sometimes the power of purchasing other goods which the possession of that object conveys. The one may be called ‘value-in-use’, the other, ‘value-in-exchange’. I think that Adam Smith didn’t go far enough. There is another source of customer value: value-in-experience. But first, let’s review what Mr Smith had to say.
Value-in-exchange is the exchange of one form of value for another. In developed economies value-in-exchange takes the form of money being exchanged for a good or service at the point of sale. One form of value (the good or service) is exchanged for another (money). In less developed economies, barter is a common non-monetized form of value-in-exchange, goods or services being exchanged for other goods or services. Your vegetables for my leather-ware.
Value-in-exchange logic suggests that value is created by the firm, embedded in products, distributed to the market, and realised when those products are exchanged for money. From this perspective, the roles of producers and consumers are distinct, and value is created about when production, marketing, distribution and selling processes are performed by the firm.
In my previous blog, I described how Net Promoter Score (NPS) works. Businesses like it because it is a simple metric that appears to indicate how well the business is doing in satisfying its customers. Satisfied customers produce positive word-of-mouth. Let’s quickly recap how NPS is computed.
NPS is computed from the responses of customers to one question: “How likely are you to recommend (company or brand X) to a colleague or friend?” Answers are recorded on an 11-point scale where participants are clustered as follows: 0-6 = Detractors; 7-8 = Passives; and 9-10 = Promoters.
NPS is computed by subtracting the percentage of Detractors from the percentage of Promoters. If 30% are Detractors and 50% are Promoters, NPS is +20.
Here are my top 5 concerns:
1. The scale is unfit for use. NPS uses an 11-point scale. All points on the scale, with the exception of 0, are positive. Zero means there is no chance of recommending the company or brand. Point 1 on the scale, therefore, must logically mean there is a small positive chance of recommending. Higher scores mean higher probability of recommending. There are no negative points on the scale to suggest that customers are likely to give negative word-of-mouth. A preferred scale would range from, say -5 to +5 where the minus score means that the customer is likely to give negative word-of-mouth and positive scores reflect positive WOM intentions.
In this blog, I explain how Net Promoter Score (NPS®) works. In my next blog I will outline some of the criticisms that are levelled at NPS.
NPS is a simple, single, metric that many companies use to measure customer perceptions of their business’s performance. It based on the assumption that satisfied customers will spread good news about their experiences whilst dissatisfied customers spread bad news.
NPS uses just one question — How likely is it that you would recommend [your company or brand] to a friend or colleague? — to get an insight into a company’s performance through its customers’ eyes. Customers respond to the question on a 0-to-10 point rating scale and are categorized as follows:
- Promoters (score 9-10) are loyal customers who will keep buying and refer others, fuelling company growth.
- Passives (score 7-8) are satisfied but unenthusiastic customers who are vulnerable to competitive offerings.
- Detractors (score 0-6) are unhappy customers who are at risk of switching, can damage a brand’s reputation and impede growth through negative word-of-mouth.
In my last blog I explained how Gartner Magic Quadrant (GMQ) works. In this column I set out some of the criticism that has been levelled at the GMQ. Gartner aims to deliver “the technology-related insight necessary for our clients to make the right decisions, every day” and the GMQ evaluates the technologies “best suited to the evolving needs of Gartner’s clients as buyers in the market.”
So, let’s review Gartner Magic Quadrant criticism. What are some of the concerns that have been raised about the GMQ?
Many of the criteria that Gartner uses to assess vendors are of little importance to technology buyers. The following criteria would be of more interest to investors than buyers: a clearly differentiated marketing strategy; a well-defined sales strategy; a valid business model; a distinct geographic market strategy; and successful marketing execution. If you wanted to invest in tech firms, this info could be quite important. But for technology buyers and users, there is little value in them.
Buying the right CRM technology can be very difficult. There are so many choices, and the differences between products aren’t always easy to understand. Although vendors provide case studies of client applications, not many allow you to try before you buy, so there is a good deal of financial and performance risk attached to the decision. Also, if you happen to be the person making the decision, and you flunk it, you’ll experience plenty of career risk. This is why Gartner Magic Quadrant has been such a boon to buyers. Independent appraisal of CRM (and other technology products) reduces the risk. Gartner claims to be “the world’s leading information technology research and advisory company. We deliver the technology-related insight necessary for our clients to make the right decisions, every day.”
In this blog, I explain how the Gartner Magic Quadrant (GMQ) works. In my next blog, I’ll review some of the criticisms levelled at Gartner’s analysis.
Sarah Cook’s objective is to “provide practical advice, tools and techniques for managers to effectively manage complaints in their organization.” She largely succeeds in achieving her goal over the book’s 11 well-crafted chapters and 200 pages. It’ll have taken Sarah much longer to write, but you’ll find it an easy, well-structured and stimulating read about complaint management that you can get through in an evening. It’s liberally illustrated with examples, mostly from the UK and USA, and tips and checklists that readers can apply immediately in ther quest for excellent complaint management processes and outcomes.
The book’s strengths are many. I like that she stresses the importance of embedding complaints handling into a broader customer-focused organizational culture. A complaints management unit that lacks these foundations is unlikely to be sustainable. I also like that Sarah identifies ISO 10002 as a cornerstone of complaints-handling excellence. Although the book claims to be aimed at management, there is much of value in the book for members of the complaints resolution team, or as I prefer to call them, the customer retention team. For example, there is excellent content about the importance of emotional intelligence (EQ) and