You know what numbers can do for you? Have you explored the power of statistical data? Data can be very useful in making business decisions, understanding your audience’s needs, observing current trends, projecting future changes, and finding out ways to make your organization work more efficiently.
Let’s dive into everything you should consider when dealing with data.
- Purpose- Consider why you’re collecting the data. What are the human or societal problems that you are trying to solve? What results are you trying to achieve? What information are you seeking? How will this data help your business needs? Whether it is continuously measuring the impact or effectiveness of your solution so you can adjust and pivot or learning from the impacts of your strategies so you can improve in future, it is imperative to understand the need for data before you jump to working with it.Example: Say you run an online store selling clothes. What data is relevant to you? What would you want to gather about your industry and your customers? Upcoming fashion trends, purchase parity of your customers, consumer buying behaviour, channels that drive customers to your website, and more.
- Data type- A few common sets of data are health-related data (to see the impact on people), traffic data (to see the movement of people), or payments data (to see the impacts on businesses, which ultimately impact people). Consider the type of data you require to understand your audience, competitors, and business needs better.
- Data frequency- How often do you require this data? Monthly, yearly, quarterly, or weekly? Is it a recurring requirement or a one-time project? Think about how often you require these results to support your business needs.
- Put customers first- Focus on your customer needs while gathering data. Look at their buying trends while devising your customer strategy and use this data to come up with ideas to support your target market throughout their customer journey. From the first touchpoint—a targeted social ad to an email thanking them for shopping with you, build relationships to retain your customer base.
- Multi-channel marketing- It is important to connect with your customers both online and offline. A multi-channel marketing strategy can help you target your audience and create a personalized experience for them based on information like their location, interests, social media platform of choice, and more. Based on the insights, you can determine the best media channel to reach out to your customers. From newsletters and emails to social media ads and TV commercials, you can use various mediums to approach your customer during different stages of their buying lifecycle.
- Customer feedback- The most important piece of data for any merchant is customer feedback. What is it that the consumer likes about your product or service? Do they want any changes? Are they satisfied or dissatisfied with the customer service? Market response will let you know how well received your product is or not. Use this data to address changes and bring innovation.
Now let’s explore data pitfalls to watch out for.
- Accuracy- Accuracy heavily depends on the method of data collection. Of course, no data set is going to be perfect or 100% accurate or precise. You must determine what is your threshold or appetite for the accuracy and preciseness of the data you need. Part of it may also be dependent on budgets. Higher the accuracy, the higher the cost.
- Reliability- How reliable are your sources? Are they using the right tools to collect, analyze, sort, and store data? Are they the right people with the right set of skills? Reliability of the data depends on the individuals gathering it and the channels they’re using. Apart from those collecting the data, reliability also includes repeatability. Repeatability is about asking an individual to fill out the same questionnaire more than once to see if their answers stay the same or change. If the answers are the same every time, there is a minimum error, and hence high reliability. If not, there would be a high error, that is, low reliability.
- Confirmation bias- More often than not we tend to find and rationalize answers we’re looking for rather than waiting for the actual results. This is called confirmation bias. Our preconceived notions come in the way of seeking answers based on research and statistical data. This can seldom result in data manipulation or even misuse of data information.
- Privacy and security– Data comes with a fair share of risks. Data breach, stealing of data, data tampering, data duplication, and more. It is important to keep your data safe to avoid misuse. Think about the following: Is your data password protected? Who all have access to your data? Has the data been backed up? Do you have data privacy rules and regulations in place? Data must be protected to avoid serious disruptions to your organization.
