Analysing the Past to Predict the Future

As business owners and marketers, we have likely asked the question “How do we investigate and understand the past so that we can confirm our predictions for the future?” Predicting the future is important for several reasons, not least being to ensure the survival and longevity of any business. But how do we make these predictions? The answer lies in data-driven analysis and business forecasting.

In order for a business owner or marketer to start making predictions, engaging in business forecasting is crucial. According to Investopedia

“Forecasting is a technique that uses historical and present data as inputs to make informed estimates that are predictive in determining the direction of future trends”.

In short, business forecasting is a planning tool which assists businesses in analysing historic patterns which in turn enables them to make informed business decisions, manage uncertainty and risk, and optimise future results. This may be done for a variety of reasons, such as to forecast sales, expenditure, and profits, or to optimise marketing strategies.

What is Historical and Present Data?

Historical or present data is collated data relating to previous circumstances and business efforts relevant to a specific topic. The starting point of business forecasting involves collating said data from various sources in anticipation of predicting a future outcome. There are two types of data: quantitative and qualitative data. Quantitative data reflects concrete, statistical information in numbers, and qualitative data is descriptive, meaning that it can be observed but not measured. While market research was once a lengthy process involving many variables, today there are various online tools which make this process much more time-efficient and user-friendly.

How Technology Has Affected the Way in Which We Collect Data

With the advent of technology, primarily the Internet, smartphones, and Bluetooth, how data is collected and used today is far more intricate and precise than what we have seen in previous decades. For example, users are constantly connected to social media, trackable via smartphones and engage in cashless transactions. This data is instantaneously collected and collated to make your experiences more personalised and efficient. The following are distinct ways in which technology has affected how we collect data:

• Social media: Social media facilitates the creation or sharing/exchange of information, ideas, career interests, and other forms of expression via virtual networks. These platforms offer a plethora of user data, especially for business owners available through social media analytics.
• Advancement of the Internet: Evidently, the advancement of high-speed Internet has resulted in the widespread availability of data which is readily at our fingertips. Most information is just a Google search away…
• Automating data analysis: While in the past collecting data was much more of a manual process, technology has made this increasingly automated. With programmes such as Google Analytics and Survey Monkey, data collected is much more sophisticated, accurate and efficient.

A Case Study: How Google Collects Data

According to Google, it collects two types of data: information that Google collects as you use its services, and information that users create or provide to Google.

• Information that Google collects as you use its services (e.g.: Google search or watching a video on YouTube), is in turn used to personalise its services for its users. This includes things that you search for, videos that you watch, adverts that you view or click, your geographical location, websites that you visit, and apps, browsers, and devices that you use to access Google services.
• Information that users create or provide to Google which is collected and protected by Google (e.g.: providing Google with personal information when signing up for a Google account). This includes your name, birthday and gender, your password and phone number, emails on Gmail, photos, and videos that you save, documents that you create on Drive, comments that you make on YouTube, contacts that you add and calendar events.

Useful Tools to Help You Collate Data

While in the past the process of collating data was for the most part manual, today we have machines that do it for us. When browsing the internet, we leave remnants behind that enable the browsers to remember the activities and actions that we take. This includes a variety of information; from what advertisements we have clicked on to how long we have spent on a webpage. Here are a variety of useful tools to assist you collating this key data:

Cookie: An HTTP cookie is a small piece of data stored on the user’s computer by the web browser while browsing a website. Cookies were designed to be a reliable mechanism for websites to remember stateful information or to record the user’s browsing activity.

1. Tools to Collate Qualitative Data

• Google Analytics: This includes webpage session duration, pages per session, bounce rate, geographical location, source of the traffic, etc. With Google Ads, users can create and review online campaigns by tracking landing page quality and conversions (goals). Google Analytics e-commerce reporting can track sales activity and performance, such as a site’s transactions, revenue, and many other commerce-related metrics.
• Social Media Analytics: This includes page followers, when page unfollows happen, reach, impressions, post engagements, link clicks, actions on page, etc. With social media adverts, users can also create and review online campaigns by tracking goals. Another way in which to collect data; A/B testing involves comparing two variations of a page element, usually by testing users’ response to variant A vs. variant B, thus confirming which is more effective.
• Mailer Analytics: This includes open rate, click rate, bounce rate, unsubscribed, forwarded, top links clicked, peer comparison performance, age, geographical location, etc. With mailer e-commerce, users can track orders, order revenue and total revenue. A/B testing can also be used on various email marketing programmes.

2. Tools to Collate Qualitative Data

With the advent of the Internet and technologies, the line between where and how to collate quantitative vs. qualitative data has been blurred. As per its definition, qualitative data can be observed and reported on which is possible via some of the above avenues. However, there are other forms of obtaining this qualitative and open-ended information: 

• Surveying tools: Online surveying tools to capture the voices and opinions of a user base.
• Online focus groups: Online focus groups enable researchers to host discussions between several respondents through an online platform.
• Keyword research: Keyword research provides specific search data that can help marketers answer questions like what are people searching for? How many people are searching for it? In what format do they want the information?

Data Mining

Data mining is the process of converting raw data into useful information. Large batches of data are collected utilising software which identifies patterns that are analysed to forecast business decisions. Data mining involves several steps:

• Organisations collect data and load the data into ‘warehouses’;
• The data is stored and managed;
• Teams access the data and determine how they want it organised;
• Software sorts the data based on the user’s end goal and it is presented usually in graph and table form.

Some data mining tools include RapidMiner, SAS, R, Apache Spark, and Tableau.

Data Analysis

Once the quantitative and qualitative information has been collated, the data must be analysed. Data analysis involves inspecting, cleansing, transforming and modelling data to determine meaningful information to support business decision-making. Data analysis encompasses a variety of techniques and methods which contribute to various business facets and is vital in supporting decision-making for the future. Analysing data to predict future results can enable business management to produce new opportunities, increase profits and optimise organisational and marketing efforts. The four types of data analysis include:

• Descriptive analysis: Descriptive data analysis studies past data to explore what This form of analysis is often used to track Key Performance Indicators (KPIs), revenue and sales, leads, conversions, etc.
• Diagnostic analysis: Once the descriptive analysis has determined what, diagnostic analysis determines why and the reason something happened.
• Predictive analysis: Predictive data analysis predicts what is likely to happen. In this data analysis, trends are identified from past data which will create predictions for the future.
• Prescriptive analysis: Following the first three steps, the prescriptive analysis combines the found information and forms a plan of action. Here we see data-driven decisions being made.

Business Forecasting Techniques

There are a wide range of business forecasting techniques which can be implemented using the said quantitative and qualitative data. While quantitative techniques are more statistical, data-driven, and long-term, qualitative techniques can be used to make short-term predictions.

Quantitative Business Forecasting Techniques

• Trend Analysis Method: This method uses past data to predict the future while placing higher importance on recent data. The method is most effective when there is a large pool of historical data that shows clear trends.
• Indicator Approach: This technique studies the relationship between indicators involving leading indicators and lagging indicators.
• Econometric Modelling: The econometric model uses several equations to test the consistency of datasets over time, as well as the significance of the relationship between datasets. It can be used to predict significant economic shifts and the potential effect of those shifts on the company. ‍

Quantitative Business Forecasting Techniques

• Market Research: Polls and surveys are conducted with many prospective consumers regarding a specific topic to predict the margin by which consumption will either decrease or increase. ‍
• Delphi Model: A panel of experts are polled on their opinions regarding specific topics. Their predictions are compiled anonymously, and a forecast is made.

The Business Forecasting Process

Once the above data analysis and business forecasting techniques have been established, business owners and marketers may follow the following business forecasting process. Once complete, predictions for the future can be made:

1. Identifying the problem or question;
2. Identify the ideal method for collecting data;
3. Determine estimates for future business operations via the information collected;
4. The chosen model conducts data analysis and a forecast is made;
5. Ensure that you understand deviations between actual and forecasted results.