29 Mar How To Get Started With Analytics In Five Important Steps
How To Get Started With Analytics
In Five Important Steps
Written by Mark Krupnik, CEO at Retalon. Read the original article published on Forbes here.
Mark Krupnik, PhD, is one of the world’s top experts in advanced analytics & AI for retailers. He is the founder and CEO at Retalon.
If you’ve been hearing rumors that your competition is harnessing AI-powered analytics and you’re not doing so already, then you should probably worry. According to McKinsey & Company, front-runners who adopt AI within the next five to seven years will lead their competition by over 120%.
As AI becomes increasingly popular, more and more articles appear every day. Let’s cut through the noise and understand how to leverage analytics to actually benefit your business.
Here are the five important steps to help you get started.
1. Ask yourself WHY you need analytics.
Wisdom is when you stop asking “How?” and “What?” and start with asking “Why?”
Before jumping headfirst into new technologies, first ask yourself: “Why should I add analytics to my business?”
Determine the real problem you would like to solve or another reason your company needs analytics. Don’t compare yourself to the competition or try to “modernize” arbitrarily. Think about it this way: There are hundreds of lifesaving drugs on the market, but before taking any, you need to know why you are doing so. Taking the wrong drug can actually have a disastrous outcome.
So, help yourself by asking:
• What is the business reason for the change?
• What is not working today?
• What is the ideal outcome?
• What will happen if you don’t address this problem?
Of course, identifying specific pain points in your business can be difficult. Hiring an outside analytics consultant is a good way to get an unbiased, expert review of your business’s needs. I’ve heard some protest, “An outsider doesn’t know my business as well as I do.”
Maybe so, but this response reminds me of a parable about a captain whose ship, caught in a terrible storm, began to sink. A small rescue vessel approached, but the captain protested: “I’ve been a successful captain for over 30 years; I don’t need your help.”
“You have been successful for 30 years,” the rescue vessel’s captain replied, “but this is the first time in a sinking ship. We deal with sinking ships every day.”
Similarly, an experienced consultant is practiced at identifying opportunities where analytics can streamline operations, cut costs and increase profit. Once you’ve determined why you need analytics, communicate this to the rest of the company. A unified company roadmap is the only way to achieve real success.
2: Set success criteria.
Getting everyone in your company to buy into your roadmap can be much easier if you have a tangible definition of success. Now, it’s your turn to get analytical. You need to answer the question: “What tangible benefits will my business gain from advanced analytics?”
To answer, set KPIs to monitor whether the analytics solution is, in fact, beneficial. It’s important to measure comparable business growth that will truly show the effect of adding an analytics solution rather than looking at overall business growth.
Start by measuring the status quo, i.e., the selected KPIs, when not using advanced analytics. Set expectations of what KPIs would be upon successful implementation.
Setting concrete, unbiased goals for assessing success will help you understand the real value added.
3. Get ready.
As with any new endeavor, it’s important to be well prepared.
There is no point in using an analytics solution if you have no data to analyze. In the same way, the best analytics solution will be ineffective if you don’t have the people in place who know how to run it. Having the necessary resources in place is key to successfully implementing analytics.
Most commonly, you will need to:
• Make sure your data is available and accessible.
• Figure out if you will need a dedicated individual or team.
• Establish relevant processes and set expectations.
• Maintain clear communication regarding the project roadmap.
4. Find the right tool.
Today finding an analytics solution is a well-defined process if you know what problem you are trying to solve.
Start by creating a request for proposal (RFP) based on the work you already did in the first three steps. Outline your needs and expectations, which help quickly weed out vendors who are not a good fit. The advantage of an RFP process in the areas of advanced AI is that the solutions that really work can and should demonstrate the specific tangible ROI on your data.
There’s no need to look into new technologies unless you would like to cure your curiosity or support academic research. Instead, look for the solutions to your specific problems, specifically designed for your industry. They most likely exist.
Once you’ve selected a few vendors, ask them to run a proof of concept. This may take some time and resources, but it’s important not to cut corners here. While working on the proof of concept and its implementation, make sure vendors are both knowledgeable and that you can communicate with them easily. A good relationship with your vendor goes a long way toward smooth implementation and a happy team.
5. Start slow.
Crawl. Walk. Run. It’s counterproductive and risky to affect your entire business at once. Start with the problem you need to cure the most. Track the progress. Measure success based on your KPIs.
Once you’re happy with the results, move on to another business segment, issue or solution module until you’ve optimized as much of your business as possible. With each new project, though, take your time and be thorough, walking through each of these five steps.
Getting started with analytics isn’t hard when done right.
If you follow this process, you will know exactly why you have chosen to use analytics, what to do in each step, what to expect and how to measure success.
There is a lot of activity surrounding analytics, AI and other trending buzzwords. The good news is that you now know where to start, and, hopefully, you can avoid unnecessary mistakes by following the above process.
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