Harnessing The Power of Data Analytics for
Small and medium-sized enterprises (SMEs) make up 99.9% of all UK businesses and 60% of employment. Many SMEs operate in areas of high specialisation, and this is their strength. However, SMEs face understandable challenges when it comes to increasing productivity, marketing their products or services or finding new sources of competitive advantage. With limited financial, time and specialist knowledge resources, it is difficult to compete with larger companies. It can also be frustrating to try and reach potential customers who you know you can serve well if you could only reach them.
Data, analytics, digital technologies and AI are a game changer, increasing competitiveness and profitability for companies that use it well. However, sometimes it appears that the power and benefits of data and advanced analytics are currently mainly the preserve of big business. In reality, research shows that data and the information and insights it unlocks is more beneficial to SMEs than to larger companies (1), helping achieve any business goal, more efficiently and effectively than otherwise possible. SMEs who lack the inhouse awareness, resources, confidence or skills are limiting their utilisation of this excellent source of game-changing business benefits. The good new is that this doesn’t need to be the case because digital technology and data analytics are simultaneously becoming more advanced, more accessible and more affordable for businesses of all sizes (2). At Mason Analytics, we are passionate about utilising the latest data, digital and AI technologies and making them accessible to any business who wants to benefit from their use – big, medium or small.
This article briefly explores the power of data and analytics for SMEs and how Mason Analytics can help you to achieve your company’s goals cost effectively and increase customer satisfaction.
What is Data Analytics?
Many businesses have increased the number of sources of, and volume of data in each source. Whether it be from social media, user behaviour on a company’s website, search engine optimisation (SEO), online marketing such as Google Ads, online payment systems and many other information systems that individual businesses use, generating these data streams is an important and necessary first step. The next step is collecting, analysing and utilising the insights produced from data sources to assist decision-making and business outcomes.
Data analytics is the process of examining data sets in order to uncover valuable insights and inform decision-making. This can involve gathering, processing, and analysing large amounts of data using specialised tools and techniques, such as statistical analysis, data mining, and machine learning. The goal of data analytics is to identify patterns, trends, and correlations within the data that can help organisations make more informed decisions and drive better business outcomes.
For example,an e-commerce retailer could use data to produce personalised product recommendations based on customer browsing and purchase history, boosting sales revenue and improving customer satisfaction. Or they could use data analytics to optimise stock levels and reduce carrying costs by identifying slow-moving inventory and maintaining optimal stock levels for popular products.The possibilities for improving outcomes are limited to the data available and the imagination.
How data analytics works
Data analytics is critical for SMEs looking to stay competitive in today’s digital landscape. According to a study by McKinsey, companies that leverage data analytics are 23 times more likely to acquire customers and six times more likely to retain them.
The process of data analytics involves several key steps: data collection, data processing, data analysis and finally data visualisation, where insights and information are shared. First, businesses must identify the right data sources to analyse, such as customer feedback, sales data, or website traffic. Once the data has been collected, it needs to be cleaned, processed, and analysed using specialised tools and techniques like statistical analysis, data mining and machine learning. The results of the analysis are then presented in easy-to-understand visual formats, such as charts, graphs and dashboards.
Data analytics can be a complex process, and many businesses may feel overwhelmed or intimidated by the prospect of analysing their data. However, partners like Mason Analytics can help businesses navigate the data analytics process with ease. MA provides in-house expertise and support throughout the data analytics process, from identifying the right data sources to presenting the results in easy-to-understand visual formats. Together we can unlock the full potential of data analytics for your business, putting you in control of your business data, and using it to its fullest potential.
Types of Data Analytics
Adoption of data analytics can provide SMEs with a competitive advantage, for example, enabling them to develop a new data-based business optimisation that could lead to increased revenue and profit. Discussing the differences in these types of data analytics helps to highlight that businesses don’t need to be turned off by the perceived difficulty of implementation. Instead, they can simply step from one type to the next, based on their starting point, each providing benefits.
There are four distinct types of data analytics, namely,descriptive, diagnostic, predictive, and prescriptive, based on the level of analytic maturity in a business. These four stages and associated factors (e.g., value, complexity and analytic questions).
Level 1 Analytics – Descriptive Analytics
Descriptive analytics is the most basic form of data analytics. It involves analysing historical data to identify patterns and trends. This type of analytics answers questions like what happened, when it happened, and how many times it happened. Descriptive analytics is useful for SMEs to gain insights into past performance and make decisions based on data.
Level 2 Analytics – Diagnostic Analytics
Diagnostic analytics is the next level of data analytics. It involves analysing data to understand why something happened. This type of analytics answers questions like why did sales decrease in a particular region, or why did customers leave a particular product. Diagnostic analytics is useful for SMEs to identify the root cause of problems and make data-driven decisions to address them.
Level 3 Analytics – Predictive Analytics
Predictive analytics takes data analytics a step further by using statistical modelling and machine learning algorithms to make predictions about future events. This type of analytics answers questions like what is likely to happen next, and what could happen if a particular decision is made. Predictive analytics is useful for SMEs to make proactive decisions, optimise operations, and gain a competitive advantage.
Level 4 Analytics – Prescriptive Analytics
Prescriptive analytics is the highest level of data analytics. It involves using data, statistical algorithms, and machine learning techniques to identify the best course of action to take in a given situation. This type of analytics answers questions like what should be done to achieve a particular outcome, and what actions will lead to the best results. Prescriptive analytics is useful for SMEs to make data-driven decisions that are optimised for the best possible outcome.
Adoption of data analytics can provide SMEs with a competitive advantage, enabling them to develop new data-based business optimisation that could lead to increased revenue and profit. For example, by using predictive analytics, an SME can forecast future demand for their services and plan their resources accordingly. By using prescriptive analytics, an SME can identify the best course of action to take in a given situation, such as which products to offer, what pricing strategy to adopt, and how to allocate resources.
The Benefits of Data and Analytics for SMEs (3, 4)
Data analytics offers numerous benefits to SMEs across different aspects of their business. In terms of decision-making, SMEs can use data-driven insights, business intelligence, forecasting and risk management to make informed decisions. For marketing, SMEs can improve their effectiveness by implementing targeted marketing, measuring ROI, improving customer experience, and conducting competitive analysis. Data analytics can also help SMEs increase their efficiency and productivity through optimising processes, automating tasks, implementing predictive maintenance programs, and real-time monitoring. These benefits have been demonstrated by real-world examples such as a local restaurant chain and a manufacturing SME in the UK. In future articles we will explore each of these benefits in detail, to provide an opportunity for you to identify where you can most benefit your business, but an overview of some of the key areas follows:
Make better business decisions
Small and medium-sized enterprises (SMEs) can leverage data analytics to make informed decisions. For example, a retail SME may use data analytics to identify customer preferences, such as product categories and price points, and adjust their product offerings accordingly. Similarly, a manufacturing SME may use data analytics to track inventory levels, production times, and other KPIs, identifying areas for improvement and making data-driven decisions to optimise their operations.
Improve marketing effectiveness
Data analytics can help SMEs create more effective marketing campaigns. For instance, a small restaurant chain in Birmingham utilised data analysis tools to monitor social media conversations and track customer sentiment. By incorporating customer feedback into their marketing campaigns, the chain saw a 15% increase in bookings and a 20% increase in customer satisfaction. Similarly, by tracking KPIs such as click-through rates and conversion rates, SMEs can identify which campaigns generate the most revenue, and optimise future campaigns for a better return on investment.
Increase efficiency and productivity
SMEs can increase their productivity and efficiency using data analytics. For example, a manufacturing SME in the UK used a cloud-based inventory management system to track raw material inventory and production times. By optimising its production schedule, the company increased productivity by 25% and reduced inventory costs by 15%. SMEs can also leverage automation tools like robotic process automation to free up employees for higher-value tasks, and real-time monitoring to quickly identify and address issues that could impact productivity.
Create competitive advantage
Data analytics can provide SMEs with a competitive edge. By analysing competitors’ marketing campaigns and customer behaviour, SMEs can identify gaps in the market, emerging trends, and opportunities for growth. For instance, a local restaurant chain used data analytics to track customer behaviour and tailor its marketing campaigns to attract more customers during off-peak hours. As a result, the chain saw an increase in sales and customer satisfaction. Similarly, SMEs can leverage data analytics to gain insights into their own operations, optimise processes, and improve marketing campaigns. This can result in increased efficiency and productivity, and ultimately, a competitive advantage.
SMEs cannot afford to risk getting left behind ignoring the power of digital marketing and data analytics. By partnering with a company like Mason Analytics, SMEs can gain access to top-notch digital marketing and data analytics solutions that are tailored to meet your individual business needs. Our comprehensive digital marketing and data analytics services includes data and analytics, content marketing and SEO and marketing strategy services. Contact us today to discuss how we can help your business succeed.
2. M. Willetts, A.S. Atkins, C. Stanier (2020) A STRATEGIC BIG DATA ANALYTICS FRAMEWORK TO PROVIDE OPPORTUNITIES FOR SMES, INTED2020 Proceedings, pp. 3033-3042. http://eprints.staffs.ac.uk/7081/1/893_MW_ASA_CFS%209Jan2020.pdf
4. Trends of digitalization and adoption of big data & analytics among UK SMEs: Analysis and lessons drawn from a case study of 53 SMEs https://arxiv.org/ftp/arxiv/papers/2002/2002.11623.pdf