Trying to implement analytics in your business is as challenging as getting started on a “healthy” eating plan. We all know that collecting and examining data is supposed to be good for you, but there are so many opinions about how to go about it, and they’re continually evolving.
Once upon a time, a healthy diet simply meant eating your vegetables and avoiding excesses in the dessert and alcohol departments. Nowadays, however, there’s a diet guru on every corner advocating for their own peculiar regime—low fat, low carbs, gluten-free, grain-free, sugar-free, based on your body type, based on your blood type, and so on and so on.
Likewise, it used to be that gathering data in business meant conducting a customer survey, doing an industry analysis, or spying (discreetly) on your competitors. Now, with technology shaping the way business gets done, collecting and interpreting data are no longer discrete or straightforward activities. And there’s an army of application developers, consultants, and practitioners, each with their own specialized approach to harnessing data’s power.
As the leader of a small business, you might be tempted to put your head down, ignore the analytics “buzz,” and wait for the hype to blow over. But analytics is no Grapefruit Diet. It’s not a passing fad but a new, permanent piece of business operations that small businesses need to master to keep up with their customers and their competitors.
As you consider the ever-expanding list of technologies to enable analytics, it’s helpful to consider both the potential and the limitations of these tools. Getting clear on both their promise and their pitfalls will help you make smart decisions about when and how to implement analytics to improve your company’s overall performance.
What is data analytics in a small business context?
Data analytics isn’t just one activity. It’s a combination of business processes and technology that enable an organization to collect, store, manage, and interpret data:
- Data collection can happen manually, through automation, or through a combination of manual and automated processes. For example, you might collect data on your customer experience through a survey you hand out in person at the local shopping mall, through a survey you distribute through email, or through both media. Similarly, you might collect data on your website visitors’ preferences through an Internet tool, and you might also interview customers who have placed web orders.
- Data storage can take place on individual computers, customer servers, or in the cloud, or through a combination of these. For example, each of your sales associates might keep a log of sales calls in a spreadsheet on their laptop. Or they might use a CRM (customer relationship management) platform, such as Salesforce, to store that information.
- Data management can take place manually, but more often happens through a software application. Companies intent on collecting and storing data quickly find that the volume of information outpaces the ability of employees to sort it and keep it up to date. For instance, let’s say your sales reps have just started logging all the notes from their sales calls into Excel sheets. As they develop this habit, and as sales increase, they’ll soon find they need something more sophisticated than a spreadsheet to organize their data into meaningful categories. At that point, you’ll probably want to invest in a CRM.
- Data interpretation requires both brain power and computing power. Analytics tools make it easy to search a data set for answers to key questions. They also enable data visualization, the display of data in easy-to-read formats, such as simple, colorful charts. At the same time, you get from an analytics tool only what you request of it. In other words, the human ability to ask pertinent questions largely determines the quality of the results the tool delivers.
Large companies are now hiring data scientists to help them with these four aspects of business analytics, but that doesn’t mean you can’t do analytics on a small business budget.
If you have a website and use email marketing software, then you’re already well-equipped to start taking advantage of significant amounts of useful data. For example, basic Internet tools can give you data on how many people are visiting your website, when they’re visiting it, where they’re visiting from, and how long they’re spending on each page. Most email marketing software includes analytics functions to help you track which subject lines get the most opens, who’s opening your messages, and which links they’re clicking on.
As your business becomes more digitally mature, your analytics capabilities will grow accordingly. For instance, graduating to an email marketing tool that integrates a CRM (like Hubspot) will give you deeper insight into customer behavior.
Keep in mind that analytics don’t apply just to externally-focused activities. You can also use analytics tools to monitor your business processes, such as fulfilling orders or managing projects. Again, as you integrate more digital solutions into your business, you’ll find new opportunities to take advantage of analytical insights across various business functions.
The essence of business is making wise, timely decisions. Until recently, that meant making mission-critical choices based on little more than hunches and hearsay. The fundamental promise of analytics is that it offers a rational alternative based on up-to-the minute data.
Until analytics came onto the scene, even when business leaders thought they were consulting data, they were often deriving opinion from stale or incomplete information. If you’ve ever used industry reports to drive your planning, then you’ll have experienced this dilemma. Even the most current of these supposedly cutting-edge documents typically draw on research that’s six months to a year old.
If you’re not using up-to-date information to make decisions, you’re likely driving your business using emotions as your guide. You’re also vulnerable to the cognitive biases that affect us all, such as:
- Confirmation bias (favoring information that validates our existing beliefs)
- Anchoring bias (allowing the first piece of information you learn to set expectations)
- Attentional bias (paying attention to some aspects of a situation while ignoring others)
- Optimism bias (believing you are more likely to succeed than others)
- Halo effect (letting your overall impression of a person, such as their physical attractiveness, determine your sense of their character)
While using data analytics won’t completely eliminate such biases, it will help keep you out of the mental ruts we tend to fall into without self-conscious monitoring. Data analytics provides a set of corrective lenses through which you can view and analyze your business, using “facts,” not personal fiction, as your guide.
Through data visualization, analytics tools also enable your whole team to see through those lenses at the same time. This generates more focused, informed discussions, resulting in smarter decisions.
Business analytics gives you a tool you can use to improve business performance. But like any tool, it requires a skilled operator. Otherwise, to quote those ads for too-good-to-be-true diets, “results may not be as shown.”
A recent Harvard Business Review article points out the hazards of relying too much on technology and not enough on human powers of critical thinking. In “Why You Aren’t Getting More from Your Marketing AI,” a team of academic and industry researchers points to the art of questioning as the weak link.
If you’re not asking the right questions, you won’t get truly valuable answers, no matter how robust a data set you’ve amassed. While analytics can help predict future patterns, it doesn’t operate on the user’s intuition, like a crystal ball. Analytics tools don’t read the mind of the user; they require thoughtful programming to produce helpful results.
The HBR researchers give an instructive example. They relate the story of a gaming company that wanted to know how to encourage gamers to spend more during play. Assuming that greater engagement would lead to greater spending, they used analytics tools to predict how to increase engagement. However, their initial assumption proved false. Following the predictions, they increased user engagement—but that didn’t result in greater spending. Turns out they were focused on the wrong question. Rather than asking “How can we increase player engagement?”, they should have asked, “How can we increase player spending?”
The peril of assuming the technology will do all the work can become magnified when you’re working with business analytics as a team. Just as data visualization tools can facilitate information-sharing, they can also enable groupthink, the tendency of a group to arrive at a unified view, without adequate debate.
To counteract these pitfalls, aim to integrate analytics into your strategy and operations using a balanced, SMART approach.
A SMART Approach to Analytics
You’ve probably heard of the SMART system for setting attainable goals. For small businesses, SMART analytics provides a similar set of simple guidelines, but the acronym spells out a different set of success criteria.
Regardless of the technologies used, a SMART approach to analytics fulfills five basic requirement. It’s:
- Sustainable—Be careful not to bite off more than you can chew. Setting up and maintaining analytics systems takes time and effort, which you’ll need to continue on an ongoing basis. Better to start simple and sustainable than to tackle too much at once and find your team becomes overwhelmed by the data upkeep.
- Measurable—Make sure the data you’re collecting and analyzing is truly measurable, and measurable in consistent ways. If you can’t measure it and enter the measurement into your analytics tool, it’s not a worthwhile data point.
- Aligned—Even in a small business with simple systems, it’s easy to quickly collect vast amounts of data. This is especially true if you’re starting your analytics journey by digitizing paper records. While it’s tempting to start gathering and measuring every bit of information you can lay your hands on, be choosy. Measure only those data points that align with your strategic goals. Make sure you’re driving the data, rather than allowing it to drive you.
- Results-oriented—Leverage data to create Key Performance Indicators (KPIs) you can use to measure your progress on specific strategic initiatives. Scientists may collect data just for data’s sake, but any data you and your team collect should target a specific outcome.
- Team-friendly—As with any other shift in direction, introducing analytics into your business requires a change management strategy. Take time to prepare your team for working with analytics, providing training and support as necessary. Overcommunicate the value of analytics, repeatedly reinforcing ways that it’s helping to improve the employee and customer experience. Most importantly, be sure to celebrate the wins that result from the new focus on data.
At the end of the day, running a business anchored in data is a discipline rather than a strategy or a method. The more you engage with analytics, the more you’ll find your thinking shaped by the habit of looking first to the data, then to opinions. As a result, you’ll strengthen both your thought processes and your leadership.
 Ascarza, E., Ross, M., & Hardie, B. (2021). Why you aren’t getting more from your marketing AI. Harvard Business Review, 99 (4), 49-54.