Gone are the days when large organizations with adequate cash reserves invested heavily in Data Analytics projects. During the last decade, SME’s are realizing the importance of exploring data projects and are starting to invest in them. Since most analytics projects start out by trial and error and a mountain of uncertainty, businesses that started much earlier are now far along the experience curve (Their return on investment and effort is a lot higher now compared to when they started) than those who are venturing in now.
Since we specialize in handling projects for organizations who are starting to invest time, money and energy in analytics projects, we regularly come across identical problems that businesses face while setting up processes and systems in place. In this blog, we explain the top 5 problems that SME’s run into and how to navigate away from them
1. NOT starting a project with a concrete goal
When we meet with businesses for the first time, our very first question to them is always – “What problems are you facing that you would like us to solve?”. The first priority of any analytics process should be to answer a specific business question or problem. All other priorities should take a step back initially.
Mike Le, co-founder and COO of CB/I Digital, says it best, “We see too many web businesses that don’t have even a basic conversion tracking setup, or can’t link their business results with the factors that drive those results. This problem happens when businesses don’t set a specific goal for their analytics. When you do not know what questions to ask, you cannot know what you'll get … If your goal is to maximize online sales, you’ll want to track order volume, cost-per-order, conversion rate and average order value. If you want to optimize your digital product, you’ll want to track how users are interacting with your product, usage frequency and the ‘churn rate’ of people leaving the site. When you know your goals, the path becomes clear.”
If you are not asking the right question, you probably aren’t even capturing the right data to begin with!
2. NOT defining and capturing metrics
“A business metric is a quantifiable measure that businesses use to track, monitor and assess the success or failure of various business processes”
Either businesses completely fail to capture metrics in their analytics programs or capture too many that it dilutes the focus on core business performances. The point of defining and capturing metrics periodically is to understand, after removing all the noise, the direction the business / department / product or service / customers need and want / etc. is heading towards. Once the fundamentals are in place, it is acceptable to go broader.
The metrics captured should also change and evolve as the business grows. They should be able to demonstrate the current state of the business or concern, not a historical one. Metrics should also be allowed to evolve and grow as the business grows, ‘allowed to’ being the key term here. The only thing that businesses need to make sure is that the metric is always capturing what it is meant to and is not out of date.
Remember, fancy data is always never the solution. Stick to fundamentals, update continuously and keep it simple!
3. NOT understanding the point of data visualization
It is very crucial to understand why analytical processes end with visualized reports that are in an easy to understand format. From our experience, managers spend a ton of energy on trying to make reports appealing and eye-catchy but aren’t able to derive business value from it.
This is primarily because the visualization is meant to be eye candy and not interpretation friendly. Metrics that are not meant to be measured together are often matched together solely because they serve the purpose of making the visualization stunning. The first and only objective of visualized data should always be to make interpretation of the output extremely easy (which already involves making the visualization easy-on-the-eyes). Always, the goal of visualized data should be too –
Comprehend output quickly to encourage faster decision making
Identify relationships and patters in data
Capture emerging trends in product sale and customer data before your competitors
Be able to easily communicate the output and story to your seniors and subordinates
If you are prioritizing other aspects, you are perhaps on the wrong lane. Now you know why!
4. NOT understanding the importance of ‘error rate’
The gap between usable insights and false outputs is the error rate. Often times decision makers do not have the necessary skill to track or comprehend the importance of capturing data errors. They are only provided with the reports and are asked to trust the output without a doubt.
Without capturing errors or questioning processes, there are too many factors that can go wrong. Most times, the output can look positively convincing while the truth is a completely different story altogether. Especially in the last 2-3 years or so, the ability to capture and analyse data, at an affordable cost, is incredibly easy and pocket friendly. However, fail safes are not put into place to capture erroneous data entries. What we tell our clients is that if erroneous entries are less than 3% of your data set and less than 3% of your cumulative ‘column-data’ (Remember that it is an ‘and’ and not an ‘or’), only then is it completely acceptable to remove those row entries and proceed with the analysis. Otherwise it is not advisable and data collection and capturing processes need to be revisited.
If you have a doubt, stop using the data to make decisions and when you have a question, ask them! Remember,
Ask the right questions
Use the right data and
Implement the right analytical processes
5. Copying other companies’ methods and reports
When we ask businesses about why they started doing data projects in the first place, the most common answer is always, “We heard about how it transformed our competitors’ business and we want to do the same here. So, we are trying to accomplish analytically all that our competitor has done”
Businesses that copy from other businesses most likely see unclear returns from their analytics investments. This is because, believe it or not, both businesses (yours and your competitors), irrespective of how similar you product or service offerings are, are entirely different! Involving in analytics programs that solve your businesses related concerns should be your highest priority. Ask yourself what business problems you are facing and how data can solve those issues. Once that is in place, then it is acceptable to look at your competitor’s analytics efforts to see what you can copy.
Remember, the knowledge and expertise required to set up analytics is complicated and investment in the right tools and systems is expensive. Therefore, it is preferable to get it right the first time and not through trial and error!
Being a business in the year 2020, having an analytics program is a must-do and not bucket list item for businesses. If you are going to fully capture the performance of your business or vertical, understand customer needs and wants, cut down expenses to improve your bottom line and derive value from all new ventures, it is highly recommended that businesses invest in analytics starting today. The more you postpone, the more your competitors are edging ahead of you.
Involve yourself in analytics, be inspired by it and start innovating business practices. It’s that Simple.