According to a 2019 study by data consultancy NewVantage, 91% of Fortune 1000 companies are investing in big data, analytics, machine learning and artificial intelligence. Put simply, the world's largest businesses are finally making a concerted push into this realm – and not before time, either.
Indeed, in the coming years, a diverse array of corporations – from American Express to Johnson & Johnson – will be major players in data analytics. But what about small and medium-sized businesses (SMBs)? If you utilise more of your budget to leverage the power of data analysis, what are the benefits for you?
Whether your objective is to improve everyday operations or boost the bottom line, data analysis in business can influence, drive and identify demand, allowing enterprises to search for patterns and enhance decision-making abilities. To illustrate how, here are seven ways your organisation can use data analytics to enhance and improve your business intelligence capabilities.
In today's one-hour dry cleaning, instant messaging, 5G world, everyone wants immediate results. For businesses, it can be challenging to strike a balance between quantity and quality; your time cycle for decision making diminishes. In other words, you will need to respond more quickly to an issue than ever before if you want to maximise opportunity.
Is it impossible? Not quite. Data analytics responds to the urgency and alleviates the strain by extending your firm the information, enabling you to take the correct – or at least the most advisable – route.
For example, if a client has a billing issue that could take hours to remedy, then your integration of conventional business intelligence and data analysis can reduce that to minutes. Not only does the customer's query gets solved, but it could also potentially increase your retention rates, which is imperative.
Because the whole world has gone digital, there has never been a better time to be in market research. Data analytics are proving to be an asset for an organisation's marketing efforts, from gathering data to testing and measuring campaigns, to optimising strategies. What makes it beneficial is that data analysis coalesces quantitative and qualitative data, which is then placed under a spotlight to produce actionable insights and research relevant conclusions.
In a market research context, data analysis can:
- Determine the total size of your market.
- Identify key demographics of B2B or B2C customers.
- Identify where your potential leads originate.
- Display trends in your target market (growth rates, consumer preference, product development).
- Identify consumer needs, beliefs and values.
In the end, your market research should compartmentalise the opportunity and potential of your business and product.
It is estimated that employers can receive up to 250 applications for every job posting. However, thanks to the ease of online submissions, many of these candidates do not qualify for the job – making the recruitment process time-consuming and difficult. Essentially, you are trying to find a needle in a haystack.
As a result, hiring managers are increasingly incorporating analytics into their recruitment strategy. While it is true that not all metrics provide the same level of value, it is still pertinent for companies to adopt a metric-based system when using data analysis. Your firm should focus on four key areas when integrating data analytics into finding the right candidate:
- Data collection
- Application funnel
Once you have introduced these elements into your hiring strategy, you need to pay attention to several metrics that can ensure you are improving your methodology:
- The volume of applicants and hires.
- The cost per lead, cost per applicant and cost per hire.
- The lifetime value, or LTV (the metrics will be subjective and vary by each company).
According to the US Census Bureau, there are more than 30 million small businesses in America, while in the UK, there are nearly six million private sector businesses. What does this mean? Mainly, that it can be hard for startups to stand out from the crowd, making brand awareness strategies more essential and needed than ever.
So, how can data analysis improve your outreach tactics? Here a few ways:
- Content Marketing: Studies have routinely found that young consumers enjoy content marketing, especially when it is relevant and informative. By understanding your audience and the generic makeup of your consumer base, you can produce content that is tailored to their needs, whether it is an infographic or a blog post.
- Email Marketing: No, email is not dead – it is better than ever. Data analysis can help you learn when emails are opened, what times are best for click-through rates (CTRs), if they are shared on social media and how the emails are translating to offline sales as well.
- Social Media: Facebook or Twitter – you are contending with thousands of other brands over millions of eyeballs. Instead of just randomly posting tweets on your timeline or messages on your wall in the hopes someone sees them, you can craft your social media strategy by understanding your referral traffic, which is then analysed based on the five Ws (who, what, where, when and why).
- In-Store Tracking: Yes, big data is not just for your e-commerce model but also your brick-and-mortar efforts, too. By analysing this information, you can track the performance of your physical stores and adapt to the data, like installing in-store sensors, which can help you put together shelving displays or improve the layout of the store. Additionally, you can monitor your inventories and see what is selling better in the store than on your website (and vice versa).
Predictive analytics is the way of the future for every industry. It is an all-encompassing term that consists of data mining, machine learning and predictive modelling that help you analyse current and historical trends so you can establish predictive insights. Does it sound like science-fiction? Perhaps, but it is happening right now – and the private sector is ecstatic.
Although most businesses can benefit from predictive analytics, it is the retail industry that is leading the way, allowing the sector to determine customer responses, shopper purchases and cross-promotional strategies. This enables enterprises to attract, retain and grow their clientele while also improving operations to manage resources, forecast inventory and automate other remedial tasks.
As a business, you want to minimise costs and maximise profits; one way to achieve this is to streamline your entire operation through big data and analytics. Some of the areas that can benefit in this regard are:
- Supply Management: Supply chain analytics is a tool to improve future demand projections, as well as what your customers may need following their order, and what products are less profitable in a certain period.
- Order Fulfillment: Advanced analytics are making warehouses more efficient, decreasing the margin for invoice error and enforcing safety procedures on the ground.
- Customer Service: Data analytics can calculate your customer lifetime value by automating and personalising the omnichannel experience, allowing you to understand better your customers and how to engage them.
- Cost Savings: Big data analytics can pinpoint what is costing your company money when it comes to operations. From fuel consumption to utility expenses, big data can tell you what is hurting your budget.
Companies are always looking to trim the fat, enhance the efficacy of day-to-day operations and build upon revenue generation, and data analytics enables this in a truly unprecedented way.
Actionable insights are what you get from a thorough data analytics blitzkrieg. By extracting raw data and then employing analytical tools, you can come up with meaningful actions to ensure you are executing the most informed decisions.
Let's take a look at a few examples:
- Segmentation: Market segmentation divides your consumer into sub-groups, allowing you to better adapt to the needs and demands of your web store customers. By obtaining this information, you can then take appropriate action to update a mobile application or sell a particular good.
- Visualisation: When combing through data, we tend to home in on its external benefits. But what about internally? This is where data visualisation comes in. You take all the data you have extracted and analysed and transfer it to visual objects in graphics, such as columns, lines and points. By doing this, you clearly and concisely communicate the information.
- Optimisation: Data analytics can have a domino effect: raw data to analysis to optimisation to operationalisation. Advanced analytics can lead to positive business outcomes because you have to take a specific action in response to what you learned from the data.
- Silos: Organisational silos could be dangerous to the office landscape because they lead to the erection of barriers and walls, diminishing communication and collaboration. There are many justifications for the establishment of silos, but project managers can bridge them by sharing data that can benefit the individual and the collective.
Of course, it is impossible to predict the future, but the implementation and qualified use of data analytics can give you the next best thing. From startups to corporate juggernauts to even baseball franchises, data is now the currency on which decisions are made and strategies built, so if you're not using it to advance and grow your business, then you need to ask yourself why.
How has data analysis benefited your business? Let us know your thoughts and experiences in the comment section below!