Predicting your marketing cycles can make the process more efficient. In order to make these predictions, previous data is needed so that the target market can be analysed. Predictive analytics is the process of looking at data and making informed estimations about the future of a market.
Humans are quite predictable – we have certain routines and buying habits that don’t change. Marketers are able to look at past data and predict what the future will hold, because of our unchanging habits. That’s the reason why retail stores can guarantee that November and December will be their busiest time of the year.
How does predictive analytics work?
The idea is not new – predictive analytics has been around since the 1950s. The only difference is that computers now make the predictions, but the formulas and maths behind the process are still the same.
We can process a lot more data and numbers nowadays, in far less time, with our computers. To run a predictive analytics campaign, you need a source of data, such as Google Analytics or Facebook Insights. You also need someone capable of making sense of this data and someone who can interpret the results and act upon them.
The whole point of predictive analytics is to make use of the knowledge and act when the time is right. The accuracy of the prediction depends on the accuracy of the data itself. Having a trustworthy source of data is important, so make sure that your parameters are set correctly on Google Analytics and similar software.
How can predictive analytics help a business?
Predictive analytics is usually done by a computer as they are far quicker at analysing the data. Computers are able to make quick and extremely accurate predictions. They can make the marketing process more efficient and effective for a business.
Predictive analytics can tell business owners where to spend money and where to save. Knowing exactly which week of the year will be the most beneficial for a Facebook ad campaign will make a marketing campaign more valuable and time-efficient.
Knowing when something is likely to happen is the most useful aspect of predictive analytics. Businesses can now plan ahead and gauge when their customers are likely to make a purchase, and when they are really not interested.
The most practical forecast is set on a 52-week timeline. This graph can break down buying habits for each week of the year. By looking at search data on Google AdWords Keyword Planner or Google Trends, marketers are able to see the weekly peaks and valleys for keywords related to a business.
The more data you have, the more accurate your prediction can be. Ideally, five years’ worth of data is needed to make a good forecast with positive results for a business. Once you’ve identified the key days or weeks for marketing campaigns, the necessary planning can take place.
Two weeks before the peak dates, social media managers can ramp up their ad spend, post more often and create more live videos or social stories to prep the target market. In the run-up to the quiet dates, managers can roll back their advertising, halt their PPC campaigns and bank some of the content for peak times.
These predictions can help a business make money during peak times and save money during quiet weeks.
Limitations of predictive analytics
Essentially, there are three limitations to predictive analytics – natural disasters, something that has never happened before, and corrupted data.
Natural disasters and political upheavals can wreak havoc on markets and industries. Political unrest or physical damage to company properties are obviously bad for business, but predictive analytics can’t tell you when events such as these are going to happen.
Similarly, if something has never happened before, you can’t predict that it is ever likely to happen. Predictive analytics uses data from past behaviours to forecast future behaviours. If data is non-existent, a good prediction cannot take place.
Lastly, corrupted data (or bad data) is only going to result in a false prediction. A business needs to be absolutely sure that their data is accurate before they begin a prediction campaign. Some third-party data providers may give a business incorrect data, making the entire process a waste of time and money.
How to do a predictive analysis
Businesses with a small budget but the technical know-how, there are a number of free tools and programming codes that can be used. These tools, such as SIDEKIT, NumPy, timetk require a lot of technical experience with coding and programming languages but can be effective.
If you don’t have the knowledge but do have a big budget, then outsourcing is your best option. There are a number of data science companies that are capable of doing predictive analytics in no time, so weigh up your options and choose the best one.
A predictive algorithm can be such a valuable tool when applied in the right way. The computers can run the data but it takes human intuition and judgement to pull off the campaign successfully. If nobody acts on the resulting information from a predictive analysis, then the entire process is a waste of time and money.
If you wish to partner with someone who knows how to apply the best practices to achieve your online goals, don’t hesitate to email us at firstname.lastname@example.org.
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