Author: christopherskinner4136

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Customer Data Platforms — going beyond marketing needs

This is a great article about customer data platforms.

https://www.martechadvisor.com/articles/data-management/customer-data-platforms-not-just-for-marketers-anymore/

I agree with Paul that customer data platforms are integral. When used correctly, CDP’s should be integrated across all departments where possible and where valuable.

When CDP’s integrate predictive data sets, they become Look-a-like CDP’s, and help create a wonderful capability about who the future customer should be and can be. By being creative with your CDP, you can define the future of the company. You can find failure points far beyond the capabilities of other technologies. But you must integrate predictive data.

“As an advocate and provider of a Customer Data Platform, and as a professional whose career is based on building marketing technology, I’m about to make a somewhat heretical statement, so prepare yourself: CDPs are not for Marketers. That’s right; you heard me.

Don’t get me wrong, Marketers derive benefits from a CDP. In fact, once a Marketer has a CDP it’s likely the first time they have actually been in control of customer data! It allows them to uncover new insights, design laser-focused segments, and truly affect the concept of 1–1 marketing on a B2C scale. It’s all good. In fact, it’s better than just good. In the hands of the right Marketer a CDP can be game changing. But that’s not all it is.”

This is well put together. Definitely worth the read.

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How AI can help with business decisions

For me, AI helps most when predictive capabilities are defined well from the beginning.

Judgment to outcome are all based upon defining the problem best at the very beginning. AI, machine learning and any other thing you want to put in front must be defined well or it’s just more junk in the system.

For example, if you’re using cookie-based or session based web data inside of an AI engine, you could be informing the machine poorly. It’s something that it can’t recover from at all. Just because you have data does not mean AI can self correct and discover something great.

By understanding the question and having good predictive theories that are causal in nature, you help create great business decisions that have a solid foundation, resulting in value.

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Strategyn’s Outcome-Driven-Innovation (ODI) Process

Strategyn’s Outcome-Driven-Innovation (ODI) Process

According to the Strategyn website, ODI “starts with a deep understanding of the job the customer is trying to get done and the metrics they use to evaluate competing for product and service offerings. These metrics, a special type of need statement we call desired outcomes, form the basis for our innovation process. By knowing how customers measure value, companies can align the actions of marketing, development, and R&D with these metrics and systematically create customer value.” It might be obvious; this approach is very prescriptive. It involves both qualitative and quantitative research and leads to market and product strategy formulation. Sound great? All you consultants out there, don’t get too excited. ODI is a proprietary approach developed and practiced by Strategyn.

Strategyn is an impressive company with a clear mission and vision.

I like what they’re doing and would love for one day to add technology to their core process. Most of my career has been working for startups. The work can be challenging in the time to understand the customer is often quite challenging. Many times the product is not sufficiently developed or are you have a lot of options to work through. It reminds me of discrete choice analysis but at the product and company level. By adding a technology core to this process I think this company could go a lot faster and reach a down market consumer.

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Customers don’t just buy a product — they switch from something else

“Switchers” — Customers don’t just buy a product — they switch from something else. That something else might be nothing. In the world of disruptive innovation, a switcher could be a person who is underserved or not served at all. The transistor had no market in the mid-1950s. When Sony developed the hearing aid, they were competing against nonconsumption. You were switching from not hearing to having something that might be crummy, but it worked.

Customers don’t just leave a product — they switch to something else. It’s in these switching moments that the deepest customer insights can be found and are easiest to uncover.

My research indicates that the moment of switching is important, but it’s not necessarily where you learn the most information about potential customers. Imagine if you could model and predict what your non-consuming and consuming customers will do next with no data. What I mean is you will have no date of the past; no clicks, no cookies, maybe a sign-up but more likely not. Can you predict what people will buy based on theories? Yes, that’s the most important way to understanding a customer — rapid testing and prediction.

I’ll introduce a simple framework and a quick customer interview technique to help product managers better leverage the drivers and blockers that drive sales and churn: You’ll understand why people switch from one product to another and how you can increase the odds that the switch goes your way.

https://www.slideshare.net/lafranec/interviewing-switchers-a-reliable-shortcut-to-feature-definition-prioritisation

Understanding how traits connect to “switchers” make sure work go faster and get you to revenue sooner

The hyperlink is a set of slides on “switchers”. I’m just not the person to interview people. I think you can derive deep thinking From the interview process especially when adequately conducted and you have removed as much bias as possible. The world I come from does not allow us the time to do this. Fast-paced companies need to do something little different. That’s why invented a technology that works inside of the fast-paced environment. Again it does not solve all the problems, but it sure gets you to business a lot faster.

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Successful Teams have Common Personality Traits

What a great document to find from Harvard business review.

In summary the best teams have cognitive diversity and feel safe to explore and create. These are the teams that create the best work environment and produce results. My own research on the subject to next personality traits to successful teams and the results are very similar.

Matching diverse and skillful personality traits results in high performance teams. Often these teams are best because they’re nurtured from great executive teams as well. They don’t just happen.

Talent management teams can do a lot to balance where to place the right people. There are cases when you need safe teams that keep the lid down and focused.

Further research on this subject might indicate that emotions are not the drivers. Emotions are temporary states. Personality and more important traits are stable over long periods of time and are causal to why great teams exist.