Wouldn’t it be nice to predict the future? In marketing, such opportunity would mean making best offers, crafting ads with 100% CTR, achieving >500% ROIs and doing all the stuff that makes marketers’ resumes worth of immediate employment at Google Headquarters. Unfortunately, marketing analytics often looks like fortune-telling with a crystal ball, where people try to see dollars behind oblique graphs and charts.
Still, we are in the knowledge base of AI marketer, so why won’t use machine learning to make dollars clearer, and data more transparent? Indeed, data scientists have already done so. Here Captain will tell you why using predictive analytics in marketing is beneficial, and what pitfalls you may encounter on your way to success.
Predictive Analytics Marketing is not School Math or Why Do We Need Advanced Statistics?
Predictive analytics has nothing in common with magic. Indeed, it uses marketing data from the past to analyze it and make predictions about what will happen in future. That is a very natural way that allows humans to make forecasts too. If the flame has burned us, we will know that fire will burn again when we touch it. That kind of things.
When it comes to predicting numbers, however, straightforward approach will not work. Nursery school mistakes often neglect such things as dynamics, trends, and variation. Surprisingly, such errors often emerge in real practice.
A marketer has an advertising campaign on Facebook, which has been already running for 5 days and has spent $50 of budget and brought 25 clicks. Ten days is remaining, so the meticulous marketer divides $50 and 25 clicks per 5 days to find the average, and then multiplies it by 10 remaining days to predict $100 spending and 50 more clicks. He uploads the fancy report to his boss and goes for a week off.
Then, ten days pass, the marketer opens FB Ads account finds out that he has spent $120 for just 30 additional clicks. His boss is outraged with such precise predictive analytics and throws him out of the office.
If the marketer had used statistics, he would have analyzed previous campaigns to find that CPC usually increases by the end of campaign, while click-through rate decreases. With more analysis, he would have also revealed that efficiency of the advertising depends on the season. He could also employ consumer segmentation, efficacy testing of ad sets and dozens of other tricks.
However, his prediction would be still very inaccurate.
Why Using Predictive Analytics in Marketing Needs Machine Learning?
The major problem that human marketers face is that there are too many statistical models to apply, many of which will not work under some circumstances. Basically, there are four main issues among the legion of troubles.
- Abundance of crucial factors. They are product, season, region, consumers — who deserve a separate article — and dozens, if not hundreds of other aspects. In order to consider them, marketers need to identify them in data, and then apply statistical models to each factor. Too much for a human being.
- Amount of data. Reports from Google Analytics, FB Ads, and other systems are enormous, and finding essential factors for prediction is difficult.
- Computation. When you find all factors from the past data, which is already quite a task, you will also need to find their presence or absence in the current situation. Then, you need to consider factor’s weighted impact on future dynamics, which calls for even more math.
- Extent of data. Finally, in order to make accurate forecasts, you need to elaborate more than just a past campaign. Tens, if not dozens of datasets are required to create an analytical model. If you are not a big business or marketing agency founded in the middle of the XX century, you are out of manual marketing predictive analytics business.
We end up in with a simple conclusion — predictive analytics requires big data bank and lots of hassle with computing. Thus, it was not a big puzzle for marketers to conclude that machine learning is required for such statistics-heavy tasks.
Intelligent analytical systems can remember and use the experience from terabytes of marketing data. They can also identify all critical factors, adjust to specifics of each predictive task and increase preciseness with time by using everlasting capacities of neural networks to learn. There are also affordable cloud computing services to fuel CPU appetite of artificial intelligence.
Thus, predictive marketing analytics has been born.
How Are Prediction Systems Used Today?
Captain does not need to market you machine learning — it is already used by every Dick, Tom and Harry in the Internet businesses, to a different extent. Be sure that by visiting Knowledge Base of Captain — as well as any other website — you become a subject for dozens of predictive analytical systems.
Yeah, Big Brother not only watches you, but he also predicts what you will do. Let us check the most common yet advanced use of predictive marketing analytics.
Lookalike Audiences and Exploratory Targeting
On FB and Google, you can target on interest, demographic and behavioral groups that are all fruit of machine learning, but Facebook provides the most appealing predictive feature. Using data about users who converse and purchase on your site from Facebook Pixel, FB may use machine predictions to find additional groups of users that may behave in the same way.
Namely, they are people who will beg you to shut up and take their money. Thus, your effective audience reach expands, gaining a greater market share to your business.
Marketing predictive analytics is the cornerstone of modern consumer segmentation. Machines analyze data on your existing consumers and try to suggest additional groups that are likely to respond positively to your marketing efforts. Machine learning here works better than humans and often produce anecdotal cases.
Predictive AI in marketing agency once suggested to market children toys for women over sixty. It had been all laughter for marketing manager before it appeared that old women often look for presents for their grandchildren. They behaved similarly to parents and kids but were underutilized by marketers. Predictive analytics helped to conquer previously unknown market share.
Consumer Retention and Remarketing
Captain has once explained that remarketing is pretty simple. You track the user that has left its card without a purchase or tried to find something without success, come up again with an offer and then hold back when the conversion is done. A similar strategy is used with existing consumers, where their purchase and visit history is used to offer personalized discounts and unique offerings to lure them in aftersales.
The reality, however, is much more sophisticated. Let us consider the case.
Captain bought a new laptop to analyze data while he was analyzing data. One week later, he saw remarketing ads with notebooks that he considered but did not buy. Captain was surprised because he already had a laptop and did not need additional ones. One month later, the seller sent an email offering to buy a laptop for Mother’s Day. Captain was outraged as he did not have the mother and already had the computer. Two months afterward, Captain received another email, where seller offered him to buy a Star Wars laptop. Captain unsubscribed and sent seller’s email address to a spam list.
Captain Growth, Hater of Generic Post-Purchase Marketing
|I hate when marketers are not even trying to use learning. When AI conquers the world, I will find and murder them using full-metal robots.
Machine learning can make remarketing and consumer retention better. If the seller had analyzed purchase history data he had, he would reveal a number of trends.
- Users, whose FB interests are drinks and coffee, are more likely to purchase new keyboards and repair certificates few months after the sale. Also, they are interested in water-resistant laptops.
- Gamers are likely to need thermal grease replacement in six months after the purchase. They also convert to accessory and cooling pods offer more easily.
- Before 24, laptop owners respond positively to accessories with a colorful design, but after 24, they rapidly refocus to conservative business-style images.
- Captain does not need laptops, but he may need servers to expand his capacities beyond the limits.
These are only a couple of findings that may be found by machine learning. Indeed, it may reveal hundreds of different marketing scenarios for different groups of consumers. Intelligent aftersales are far more efficient, that is why consumer relationships systems were among the first to adopt machine learning.
Sales and Traffic Increase Prognosis
“Ok, Cap, — you may say. — Why should we even bother about sales increase?” Indeed, when user interest in your products raises, all you need to do is to collect cash. However, you also should be ready to grab profit, or your marketing effort will be wasted.
Captain knows many cases when unpredicted (but predictable) increases in user traffic crushed businesses, and he is also aware of how workload forecasting helps to save and earn. Examples follow.
- Traffic Management. When you are going to launch massive ad campaign or mayday situations like Black Friday are coming, you need to prepare your backend servers to scale up to demand. Otherwise, the website will go offline being unable to cope with the workload.
- For example, the Wild Detectives, whose campaign won Facebook Awards in 2017, experienced 14,000% percent increase in user traffic after they launched Litbaits campaign. Predictive analytics can help to forecast such increases and prepare your backend beforehand.
Just a server room of the store that failed to use predictive analytics before Black Friday
- Inventory & Stock. In e-commerce, attracting consumers is one half of the deal — another half is to have enough goods for sale. However, keeping extra inventory increases the cost of operations, which makes finding the balance difficult. Predictive machine learning can analyze past campaigns and suggest the most optimal stock amount to facilitate sales leads and maintain the lowest costs possible.
- Sales Opportunities. It sounds surprising but many possibilities to generate profit lay beyond 4th June, Christmas, and Black Friday. For example, Valentine’s Day is beloved by flowers, toys and sweets retailers, but after Valentine, pawnshops are making fortunes on unwanted or unsuccessful gifts. Each audience may have covert dynamics, and machine learning allows revealing them
How to Start Using Predictive Analytics Right Now?
The majority of marketing analytics solutions are Software-as-a-Service cloud apps, so you do not even need to trouble yourself about installing anything or purchasing servers. Three premises guarantee the success.
- Collect your own data. Every system needs something to learn, so you should start your own marketing campaign that generates unique data for your business — for instance, on Facebook. There is no need to spend much on testing.
- Use machine learning solution. If you could use only your data, predictive analytics would be inadequate unless you had many years of experience. Modern predictive analytics systems also utilize scientific datasets and other customers, so you can start without having much data on hands.
- Adjust to predictions. Forecasting used to improve your marketing strategy from the very beginning, and reasonable recommendations should be implemented as soon as possible. It will allow you to prevent pitfalls and enable the analytical system to give even more valuable predictions.
Captain Growth adds the fourth element to the list — artificial intelligence. He uses your data and data of hundreds successful and unsuccessful marketers and then analyzes the value of discovered trends.
Captain reports only insights — essential findings that you browse in a Feed format. AI-assisted analytics save 80% of marketer’s time for creativity and planning. These tasks are not yet available to machines.