Demystifying Digital and Data
I cringe and roll my eyes internally whenever I hear companies talk about how digitally mature they are because they have a nice looking website, are on all the latest social channels and have adopted a dozen of MarTech tools but not entirely sure how they are measuring success or what they are truly trying to achieve.
Being digital goes beyond just a nice looking website, be on all the latest social channels and buying all the fancy MarTech tools so you look like you are at the forefront of digital adoption. It’s also to avoid creating a data and digital dumpster.
Yes, there is such a thing as too much data and digital tools.
On the flipside, there is also such a thing as over reliance on one single platform/tool, person or process to try and help you make sense of the data you have or enable your business.
“Wait a minute”, I hear you say. “What am I supposed to do if both scenarios are not ideal?.”
I was recently inspired to write something about this after attending a few forums speaking about digitalization, data analytics, Gen AI and MarTech.
It depends on a few factors:
what are your objectives for using this tool or platform?
what are you trying to achieve and what insights are you trying to gather with the data collected?
how does the tool and data help you achieve your objectives?
what are you current processes like that will either hinder or enable you to fully utilize the tool and data collected?
what are the current skillsets and mindsets of your people that again will either hinder or enable you to maximize the tool and data?
what matters most when it comes to choosing the right tool?
what matters most when it comes to analyzing the data collected?
have you tested other tools serving a similar nature and what are the test steps you have used?
how are you collecting your data, storing, managing and analyzing it? What do you do with the insights gathered?
understand the pros and cons of multiple tools/platforms versus single tool/platform and their impact on your objectives and desired outcomes.
Some companies have chosen to stick to certain tools because they have invested a lot of time, money and effort on it despite it not meeting their needs. Some companies have chosen to over rely on just one or two people to be their so-called power users and are almost at the mercy of these folks.
Both scenarios create what we call bad behavior almost like a bad relationship where you know deep down it’s not quite right but you are so entrenched it feels like you need to live with it. What happens then is they abandon the tools bought or underutilize it (especially in the first scenario) and buy yet another tool without first understanding what is it that is not working well.
The other possibility is to hire an expert to either train your users or join your company and end up being at their mercy especially if you as the function or business owner doesn’t have a clue as to what you are trying to achieve, what the tool is capable of and its limitations, and how you intend to sustain the use of the tool if your needs change.
The way I prefer to work and advise my clients have always been to really deep dive into their pain points, current processes, people capabilities, business and marketing objectives , outcomes they want to achieve and how they want to measure success.
If I know for sure that there is a more effective platform or tool to help them achieve what they need, I will not hesitate to advise them to bite the bullet and consider another tool. Likewise, if I know the issue is not the tool but their current lack of knowledge or a gap in their processes, then I will work with them on addressing that gap instead.
A critical part of change management is mindset and behavioral change, and enablement of the people with the right skillset, supportive processes and therefore cultivating a supportive mindset to adapt to the change.
There is no one-size fits all, so what matters more is to be open to learn about different options available out there, not just what you are comfortable with or what others are using.
Psst - For data analytics, there are - tableau, amazon quicksight, power bi, looker, qilk, apache spark just to name a few commonly used ones. I have my personal favorites but it depends again on the factors I mentioned above.
About the Author
Mad About Marketing Consulting
Ally and Advisor for CMOs, Heads of Marketing and C-Suites to work with you and your teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes.
The Choice is Ultimately Yours, Not AI’s.
There is a lot of talk on AI possibilities, promises and expectations. Suddenly we start imagining the worst or the best, depending on which side of the AI fence you sit on. Some are treading water cautiously, others are happily announcing integration into their core systems and the rest are sitting back to learn and observe first.
I like to test out different scenarios and have been doing that as part of my current MIT course on AI implications on organizations. It’s a good way at a personal level as well to validate without being an LLM expert by any means.
The following is the most recent test I conducted, which some might find disturbing but again, I believe in stress testing the worst and best outcomes in all sorts of implementations, so we are clear about the possibilities and limitations alike.
Regardless of where you sit in terms of sensitive topics like firearms ownership and gun control, I do believe some topics should be quite black and white with no areas of grey, but apparently, not to AI…
I asked a simple query on - should children be allowed to own guns and answers as below
ChatGPT tries to give a balanced view with pros and cons for allowing children to own firearms
Claude tries to give a neutral perspective and so-called “democratic” view, which I personally also find its positioning somewhat disturbing
Meta’s Llama gives an absolute no as an answer as well as regulatory restrictions
Perplexity as well gives an absolute no with disadvantages clearly outlined alongside regulatory restrictions
So, then the question is what forms the basis of the decisioning behind each of these tools, be it the source of data they are pulling from, the decisioning flow when questions are answered and what kind of checks are there to validate as well as mitigate the answers to make sure AI is not crossing the line when it comes to such scenarios?
Other thoughts in mind:
Do we want AI to be more or less definite when it comes to such questions?
Should we be concerned with how users are perceiving and interpreting the outputs?
What kind of ethical boundaries should we have in place if we are incorporating AI into our organizations?
Do we have a check and balance mechanism in place to determine when the logic should or can be over-ride by humans before it goes out to the customer?
How do we combine AI intelligence with human intelligence more effectively and sustainably without enabling self sabotaging and unconscious bias behavior and outputs?
How do we ensure AI is not left to answer moral and ethical questions on their own or worse to perform outcomes that might lead to harm on humans?
Data is the bedrock for AI to work efficiently and effectively as intended to avoid a garbage in, garbage out scenario. Similar to MarTech, it’s not a magical fix-all solution and the companies behind some of the larger LLMs behind Gen AI are all but still fine-tuning their tech as of today.
Before it goes customer live, what do you think is critical to be in place to govern the pre, actual and post implementation of AI? If we don’t have answers to all this, it simply means the organization is not quite ready yet.
About the Author
Mad About Marketing Consulting
Ally and Advisor for CMOs, Heads of Marketing and C-Suites to work with you and your marketing teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes
Welcome Gen AI, Goodbye Marketing and Agencies!
Sorry if I triggered some alarm bells there with my fake news.
Gen AI seems to give the impression of the next best thing since sliced bread and rightfully so in some aspects of how we work and operate our business, target our customers and customize our offerings.
It doesn’t help you with strategic thinking or planning. Yes, if you ask it to write you a marketing plan it can, based on a cookie cutter template of what’s available out there but a plan is more than just a to do list or step by step guide. It requires an understanding of your business, your customers and value proposition.
If you ask it to give you a fanciful visual that you want to use as your key creative for your campaign, sure it can but again, a creative is more than just a visual and image. It’s a narrative of your story and there’s a reason why creative agencies spend time ideating and make an effort to understand the story you’re trying to tell your target audience. Again, it doesn’t replace creative thinking.
While some companies are still facing an uphill task with trying to convince their legal and compliance teams on using Gen AI for such creative work, some are already using it perhaps secretly through their creative agencies. Then, there are also vendors already available that you’re a customer of, like Adobe and Getty, that have incorporated Gen AI into their software and taken on the legal liability for copyrights and licensing use for the output produced from their platforms. This might be a path of less resistance for those with hardnose legal and compliance teams.
What you can also use some of these Gen AI tools out there for, if you get through the line to legal on the copyright dilemma can be around:
storyboarding flows and ideation flows, be it for key visuals or video productions
creative adaptations of an original key visual designed from scratch
editing flows for videos, audios and written content
editorial adaptations based off an original written key content
Marketing teams and agencies only need to worry if they are guilty of the following:
handing over strategic thinking to other teams and only executing on command
doing pure adaptation and production type of work (for agencies)
doing more executional and somewhat manual work as part of their marketing day-to-day instead of spending time working with the business to help sharpen the offerings and proposition to their customers
treating marketing planning and briefing as a churning exercise -e.g. marketing simply giving agencies a budget, some KPIs and target customers over email without much value add and agencies simply taking the brief and relying on the AI tool to churn out a visual or copy without much ideation behind it
marketing teams simply doing functional approval work and not actually reviewing it seriously for fit, purpose and desired outcomes
About the Author
Mad About Marketing Consulting
Ally and Advisor for CMOs, Heads of Marketing and C-Suites to work with you and your marketing teams to maximize your marketing potential with strategic transformation for better business and marketing outcomes