How Marketers Can Use Machine Learning For Demand Forecasting- Best In 2022

Machine Learning For Demand Forecasting

Although consumer preferences have been fluctuating for some time, the COVID-19 pandemic pushed the speed of change up a gear. People spent even more time on social media, swapping new ideas with people on the other side of the world in an instant and seeding new trends across regions.

The acceleration in digital interactions spawned changes in behaviors, habits, and norms, breaking loyalties and setting new expectations. It’s tough enough for marketers to keep up with the pace of consumer assumptions, but it’s even harder when consumers demand increasing customization from marketing and sales teams.

They are looking for personalized experiences and interactions that show an understanding of their preferences and interests. Among consumers, 73% think that companies should understand their unique needs and expectations, and over 90% of B2B buyers say they’re unlikely to respond to non-personalized marketing messages. All this when reading the consumer mind is more difficult than ever.

That’s where machine learning (ML) comes into the picture. ML, a form of artificial intelligence (AI) crunches datasets that are larger than any human analysts could cope with and at speeds they could never dream of achieving. With such massive datasets, ML engines can spot patterns that indicate emerging trends while they are still under the radar as far as human perception is concerned.

ML-driven demand forecasting lets you detect new fads earlier than the competition and with more accuracy, powering marketing campaigns that are more personalized, more agile, and more tightly targeted.

Here’s what you need to know to apply machine learning to your demand forecasting.

1.   Set your business goals and metrics

Like with every business strategy, your first step is to define what you’re seeking to achieve. This way, you’ll come out with actionable, meaningful insights that guide marketing decisions, instead of impressive but ultimately useless metrics.

For example, do you want short-term forecasts (i.e. under 12 months) that help you identify which audience segments are the most receptive to specific messaging or select conversion targets for a new campaign? Or are you looking for long-term forecasts that stretch over a year ahead, so you can set long-term marketing budgets or decide whether to expand into a new market?

Once you have your purpose, you can choose success metrics like which products you want to forecast, which markets you’re looking at, and what is an acceptable level of accuracy.

2.   Review your data

Next, you need to understand the raw materials you’re working with. List all your data sources, including internal sources like website analytics and external sources like social media posts. Consider how much data is structured, like purchase orders or macroeconomic indicators, and how much is unstructured like memes and customer support tickets.

Think carefully about whether you’re cut off from important data sources; it’s very common for data to end up siloed in different parts of the organization. Collecting data in a cloud data warehouse can help overcome silos and ensure accessibility to all your data.

You’ll also need to assess data quality. Is it accurate, consistent, and valid? How relevant are your datasets? Does the data have holes, gaps, and irregularities, or is it complete and detailed?

Very often, companies that haven’t set up any ML data projects before having poor quality with lots of gaps, mistakes, and irrelevancies. You’ll need to clean and process the data before it’s clear what’s in front of you. Basic data manipulations like data visualization can help.

3.   Select a machine learning model

There are multiple different types of ML algorithms that you could apply to your data, and each one can give you accurate forecasts. However, those forecasts won’t be useful unless you choose the right model for your use case.

For example, time series analysis uses data points taken over equally-spaced points in time and is applied to predict future demand. You might use this to model the demand for cucumbers in a certain geography for the next 3 months.

Alternatively, if you want to understand how pricing or weather affects demand for cucumbers, you might choose linear regression. This uses past values to predict future ones and is ideal for tracking the impact of different factors.

Other, more advanced ML demand forecasting models include feature engineering and random forest decision tree analysis. Your preferred ML model depends on issues like your business goals, data types, forecasting period, data quality, and more.

4.   Train and deploy your demand forecasting engine

Finally, you’ll be ready for the final stage: training and deploying your demand forecasting algorithm. You’ll need to prepare a set of training data, which is historical data that’s been cleaned to teach the model what to look for in real-world data. ML models use training data to learn how to recognize different patterns.

When you’ve trained your model, you’ll validate the results with different real-world data sets to set and refine parameters on different models, to find the one that’s the most accurate and reliable. Lastly, you’ll constantly fine-tune your ML algorithms so that they are both accurate, and a good match for your business needs.

ML demand forecasting can keep marketers one step ahead

With the help of ML modeling, marketers and brands can gain a deeper understanding of customer preferences and trends that helps them maintain their competitive edge. By taking the time to review existing data, set business goals, and metrics, choose an ML model, and train and deploy it, you can boost your marketing campaigns, enhance your content and messaging, and turbo-charge conversion, sales, and ultimately, profits.

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