Risk Aversion and Risk Seeking: Lead Generation and AI

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The psychology of choice applies to every individual. And to every business. Does loss aversion mean limiting leads? And is risk taking cost-effective?

If we could ensure all of our customers produced high-probability gains, marketing would be turned on its head. But in a highly competitive digital environment, certainties are rare and far outnumbered by the possibilities and the potentials.

In terms of prospect theory, the best marketers can do is weigh the odds. Most have spent a lot of time studying the pros and cons of risk-aversion and risk-seeking behaviour. Risk aversion – the fear of loss and disappointment – and risk-seeking – the hope of great gains in the face of unfavourable odds – is the red thread through all of our business strategies. 

This also applies to our (potential) customers, of course. To avert risk, they check reviews, return to brands they know, spend surprising amounts of time comparing prices, read the small print, and check shipping details. Any individual prepared to take a risk on an unknown startup will still be using risk aversion techniques – often in terms of price, innovation and availability.

Prospect Theory – As Applicable Now As it Was Then

Prospect theory tells us that we need to know more about the people who visit our websites and social media profiles, and click on our ads. The principles of Kahnemen and Tversky’s 1979 Prospect Theory: An Analysis Of Decision Under Risk  still apply to businesses today, especially with the technological advancement that is machine learning.

What personal information do we need to know about our targets?

We need to know about their purchasing behaviour. In terms of prospect theory, this means:

  • Whether they prefer the certainty of gain (reward)
  • Want a lower degree of loss (fear of losing out on a reward)
  • Are searching for something completely, staggeringly unique (want something they can’t get elsewhere).

Marketing examples in terms of reward-seekers include offering item discounts or free ebooks; for risk-seekings, the tried-and-tested solution is integrating a sense of urgency – one-day only offers. And we all know the importance of standing out from the crowd in terms of a unique service or product.

This is all very well. But only if you know who your potential customers are. Here is where machine learning is making strides. We’re not talking about personalization, but about locating and communicating with low-risk, high-gain new traffic. By targeting their preferences, we automatically opt for the risk averters. But by broadening outreach based upon a deeper psychology, we can snag the risk-seekers, too. How? A mix of the old and the new.

Marketing Science of the Future, Right Now

Very few marketers know enough about AI to thoroughly and successfully utilize it. Those that do know how important it is to implement throughout the customer journey. Understanding how machine learning plays a role in the office should be a marketer prerequisite in 2021. Would you rather undergo an operation led by a surgeon who has never used the new piece of equipment necessary for your procedure, or place yourself in the hand of a surgeon who keeps up with the latest techniques? Make sure your marketing department is AI-savvy before taking the plunge.

AI adds value to every step of the sales funnel:

  1. Prospecting
  2. Pre-approach
  3. Approach
  4. Presentation
  5. Overcoming objections
  6. Close
  7. Follow up

The majority of businesses, if they implement them at all, use algorithms to improve steps three through to seven. This isn’t enough.

Lead generation and lead qualification is risk aversion. By increasing potential customer numbers evaluating their likelihood of signing up or making a purchase, and then filtering the most likely to buy, businesses increase their chance of reward (and ROI).

Exactly how much a business invests at this stage determines its degree of risk-seeking behaviour. Unfortunately, many businesses look at artificial intelligence as a risk. Machine learning requires the right input, the right departmental knowledge, and the right investment. Either that, or a business leans too heavily on AI and expects it to do the work single-handedly.

Prospect Profiles and Qualifications

Finding and scoring prospects was a time-consuming human task up until very recent years. The scoring of prospects (to avert risk) via algorithm is still not widely implemented throughout the e-Commerce sector; signing up for AI lead generation software early on is well worth its steep learning curve.

Funnel automation is an area in which machine-learning engineers are extremely focused. These deep-learning models can be relatively simply run in accordance with a huge range of business preferences and profiles. Any Google search for AI Sales Assistants, Automated Marketing, Sales Automation or AI ICP Generation will turn up millions of results. It is up to your marketing department to use their own risk-aversion techniques and sift the duds from the high-performers.

Once the correct platform or software has been decided upon, internal staff need to be well-trained as to which metrics truly count; to err is human, to forgive a human error when implementing and depending on a crucial part of the modern sales funnel is significantly less simple.

Risk-Seeking in Lead Generation?

By integrating AI to find the right prospects, businesses limit risk. Finding competitively-priced, intelligent lead-generation software saves time, human resources and potentially wasted energy on individuals or companies not expected to contribute to ROI. There are many rewards to be gained when using specific algorithms that select potential customers based upon their behaviours.

But why do some marketing departments experience greater gains even when using the same software and offering similar products? 

They seek risk.

Any algorithm is dependent upon its input; the original input is human. Lead generation software takes as much risk away from prospect selection as possible through the building and updating of generalised profiles.

But the element of success is not only avoiding risk, it is taking risk. Furthermore, in the field of marketing-related prospect theory, risk-seeking and uniqueness usually go hand in hand.

When is a Face Wipe Not a Face Wipe?

When Kleenex first started producing disposable makeup removal towels (cleansing tissues) in the early 1920s, it did well enough. Glamour-based advertising for females of a certain age attracted many of the buyers it targeted. However, after six years of hearing individualised accounts of alternative uses by alternative targets, Kleenex changed its advertising campaigns. In the 1930s, these ads featured older, motherly females and children. Why?

After hearing (for years) that people were using Kleenex to blow their noses, it changed its target market. Ads now focused on disposable tissues that kept cold germs at bay. Any AI algorithm, just like the very human marketing professionals at Kimberly-Clark, would need time to pick up on such a trend. Sometimes, no amount of expensive pre-launch market research can bring the alternative targets to light.

Risk-taking by significantly widening your target prospect profile can provide surprising rewards. A gamble is more likely to provide dividends when investment is low. Businesses that gamble part of the marketing budget on unusual targets not only implement their own risk-seeking behaviours, they also appeal to the wish for something unique in otherwise non-targeted groups.

For men and women seeking a disposable handkerchief in the early 1920s, Kleenex was a unique solution. And much cleaner than carrying a used, cotton version around in the pocket just a short time after the Spanish flu epidemic.

Old(ish) and New

Integrating higher-risk methods of lead generation in your marketing strategy in tandem with AI lead scoring models open up the borders in terms of risk and unique product seekers. AI might be the new kid on the block, but to get the most out of it, you need longer-term players. One of these is paid website traffic. Paid traffic in immense numbers makes the data that AI algorithms rely on.

Predictive lead scoring models require big data for better decision making. And customer profiles need customers, first. This is one of the reasons why AI is often implemented in later stages of the sales funnel. Customer intent  and engagement data and the measurement of specific attributes such as location and age lower risk in terms of lead generation investment. But once this AI technology has been implemented,it is the right time to measure the behaviours of broader populations on the grandest possible scale. For startups and even many well-established companies with a website, SEO won’t bring in visitor numbers at anywhere near the same scale as paid web traffic.

Take the services of Web Traffic Geeks and Max Visits. Both of these well-known suppliers of human (not bot) website traffic generated millions of daily visitors from their own multiple, huge, websites. With the collected data, Web Traffic Geeks and Max Visits can group their global audiences into niches. Interested website owners and managers pay them to redirect thousands of visitors, usually according to niche. Niches include age-groups, locations, and topics of interest that number in their hundreds.

By allowing AI lead prediction software to run simultaneously with paid traffic campaigns, marketing risk-taking becomes more cost-effective. Through targeting less-impactful niches, businesses have the opportunity to show groups not usually selected for this product or service type and so offer a potentially unique product or service.

In addition, tens to hundreds of thousands of visitors can be profiled. The greater the lead data, the greater the predictive powers of your software. Win-win.

AI and Risk – Important Partners

It’s easy to depend on a single source for your leads or focus on specific machine-learning points on the sales funnel. But as we all know, there is never a single strategy or combination of strategies that guarantees results and alleviates all of our risk-related fears.

Take advantage of risk-averting AI, but at the same time incorporate a degree of risk-seeking behaviour. This is done by working outside the rigid AI borders without too high an investment – integrating SEO and traffic-enhancing strategies will continue to be of importance for many years to come. Only when you venture into the unknown can you discover something new.

Your product or service could become another example to be quoted alongside the advertising campaigns of one of the world’s best-known tissues.

 

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