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In this episode, we discussed the process of consumer decision making & fatigue. Then we review the different steps we all take while making a decision. We also discuss how the burden has increased on consumers to make the correct buying decisions due to the rise of online shopping. We conclude with a discussion on Ai and Machine learning tools that consumers, sellers and even healthcare professionals are using to ease the decision making process.
Let’s face it – We all make decisions, and the steps we follow are all pretty similar. We start with recognizing that there is a problem we need to solve. Next, we move on to gathering information on the topic. Then, we enter the evaluation phase, where one weighs choices against comparable alternatives to see which might provide the optimal solution or outcome. Alternative options could include: lower prices, additional product benefits, product availability, or something as personal as color or style options.
Eventually, we all enter the next step, Purchasing.
This process could take seconds, or years, but we eventually find ourselves in the purchasing phase where we are ready to act. There might have been hundreds or even thousands of tiny decisions made to get to this point depending on the product or service. This is where the decision rubber hits the road and money or a trade occurs to acquire the item or service we decided was the best fit for our situation. (Do you feel exhausted yet? Well wait there is more!)
At the very end is the post-purchase evaluation phase; where the buyer reflects on the purchase they made. The seller and the buyer both play a role in this process. But from the buyer’s perspective we typically evaluate if we made the right decision, and did it solve the problem.
The problem with modern day online shopping is often there are too many options and we are all overloaded with micro decisions that need to be made. Try as we might to use tricks and shortcuts. There are just too many options. There also too many choices we have to make to get to the purchase phase, no less the evaluation phase.
Not too many years ago, before the internet, we had salespeople in stores to help us.
Even the stores themselves acted as a filter.They only offered the products they felt most people would need, buy or were vetted. Now we often cut out the store. Or maybe the stores no longer hire qualifies sales people. Therefore, we head right to Google to do a search, which puts all the decisions back on us.
Sure. We might save a little money doing it this way. But in this podcast we argue that doing so also takes its toll on a person. When you elect to make all the decisions yourself, you do so at a cost. That cost may be hidden but it is still there. Something else had to be put aside to do all that research, some other decisions were not made, or you feel more tired after doing all the legwork to find the right product at the lowest price.
Next in this episode we discuss common customer profiles as they relate to our shopping style. There are some common consumer profiles and styles that are mentioned in research that all people seem to fit into.
However, while there are common styles, people do not fit into one style consistently. And each style adds or reduces decision making and we may find depending on the product or service we have a different style when it comes to shopping.
We discuss 8 customer shopping profiles/styles:
- Price-Value Consciousness;
- Brand Consciousness;
- Novelty-Fashion Consciousness;
- Confused by Over choice;
- Recreational Shopping Consciousness;
- Habitual,Brand –Loyalty
We discuss how which style we fit into depends a great deal on what we are shopping for. For example we may be loyal to a brand for clothes or electronics, but Price Conscious when grocery shopping.
To complicate things a little, we discussed how there are many other factors that come to play such as:
- Disposable Income
Each of these may play a significant role in how a person approaches a decision making process and which style they bring to the problem. Culture alone plays a large role, and is one that most people do not see as often unless they travel a great deal.
There are others we discuss as well, some are too nuanced to pin down and some such as peer pressure are fluid and are not a style at all.
In conclusion, we discuss how there are new tools, such as AI and machine learning. These can help people make decisions. In one example we mention how decision fatigue is even more prevalent when life and death are on the line . We also talk about how Machine learning is helping doctors review scans for cancer and other ailments, because there are not enough doctors, and the doctors are over stressed reviewing scans.
To reduce decision fatigue the machine learning programming reviews the scans first and helps filter the doctor to the most important ones. They feel this is a perfect example of how machine learning can reduce human decision fatigue, but this is only one of many out there to be discussed.
We also discussed these other examples that help sellers and buyer alike:
CRM – Ai Algorithms for Modeling Consumers Lifetime Value and Persona.
Recommendation System/Engine – AI Systems for learning a consumer’s preferences for explicit and implicit feedbacks.
Problem Solving – Using expert thinking processes and data to provide data which includes assessment and problem solving to complex problems.
Opinion Mining – AI helps provide business owners with invaluable insight about their customers. Helps predict demand. Quicker and more reliable.
Augmented Analytics – Use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist
Strategic Changes – AI allows better planning of production, managing all restrictions, reducing shortcomings in operations, and improving manufacturing.
Finally, we concluded the conversation knowing the topic of Decision Making & Fatigue is one we will need to revisit in the near future.
This problem is only going to get worse, as technology advances and there are more and more options. However, we believe it is also technology which will slowly help us get out of this mess and help us make better, faster and less taxing decisions. Exactly how and how fast that happens is a topic for another day.
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