Why Your Business Feels Chaotic & How Predictive Analytics Acts Like a Weather Forecast for Sales
How does a machine learn to guess what happens next?
A driver is about to pick you up from Pavilion KL via Grab. The application has access to real-time data about traffic patterns on Bukit Bintang on Fridays and weather forecasting models. For example, it may advise the driver to arrive at 4:45PM based on a predicted rainstorm beginning at 5PM, and heavy traffic along the way. What is going on here can simply be referred to as predictive analytics using AI technology – the application “learned” how to predict the future by analysing and identifying patterns within large volumes of accumulated data, including the driver’s last five rides, prior traffic history, and previous weather conditions.
The same is true for your business; you input your historical sales files into the system and optionally include additional information on public holidays, payday periods, and/or weather patterns. The system will sort through all this data and report, “Your sales of durian products will decrease by 20 percent next week due to an incoming monsoon.” Predictive analytics and AI remove “guesswork” from our decisions; after learning the past, we now make decisions based on “data does” instead of “I think.”
AI predictive business analytics: Stop chasing trends and start catching them
We’ve all had that experience with a customer who does not buy something they were thinking about all day on a Tuesday. They try it on for 5 minutes, put it back on the display, and walk out the store. However 2 hours later at 2 am, they are sitting in their pajamas buying that same item online from a competitor. Customer behavior prediction is part of what is interesting to me as a digital marketer. An example of this would be typical brick and mortar businesses in Malaysia that do not understand customer behavior outside of walking into the business. They do not have any insight into who has walked past their door until they walk back in.
AI tools have evolved to utilize signals and patterns. If during a period of time, (frequency), a user searches the category “wireless headphones” and does not click “buy,” the e-commerce site can flag the user. The user may have been waiting for a sale price or may have been researching. When you look at the prediction through the lens of machine learning or AI you can start to put together why the user is exhibiting the behavior they are showing; therefore, give a retailer insight into how to modify their marketing campaigns.
For example: A hardware store in Penang used data analysis to see that every Monday at approximately 8 am on each Monday after a weekend flood there is a surge of people searching for “water pumps.” Therefore, the hardware store instituted a digital marketing campaign to show advertisements of “Water Pumps” beginning no later than 7 am on every Monday and as expected saw a rise in sales. The retailer did not have to guess; they watched the trend and knew what to do.
The Core Insight
Human + MachineAI Sees the Pattern, You Make the Move
Breakthroughs in AI predictive business analytics don’t replace the Malaysian business owner’s instinct. Instead, they confirm the “feeling” you have in your gut with hard data. It turns a “maybe we should stock up” into a data-backed decision, letting you focus on customer service while the machine handles the math.
Can AI predictive business analytics really tell me if a deal is too good to be true?
We are Asians. We are traditionally very polite people. Sometimes, we will trust the “ah long” or someone who is willing to supply goods because of their nice face. However, nice faces do not pay the bills in business. Artificial intelligence (AI) risk assessment systems are changing this. These systems can be reviewed like a background check to make business decisions. Let’s imagine you own a logistics company in Johor and have received an inquiry from a new client wanting to pay after the goods were received (standard 30-day term).
The AI will cross-reference that new client’s background (payment history, health of their industry and news articles about their business) and ask itself whether this business has a 70% risk of making a late payment. If the AI determines this new business client presents a 70% chance of a late payment, it will alert you. This does not mean the new client should be turned away; it means the terms should be changed. For instance, perhaps you could request 50% of the total amount due before shipping the product. We are not trying to be mean; we are trying to be safe. Real-time business intelligence allows you to say “yes” to the right businesses or “not at this time” to the wrong businesses without offending anyone.
Why your spreadsheet is lying to you
Let’s face it. Many of us still use Excel. We have three separate files: one for inventory, one for sales, and one for commission. And they don’t connect with each other. This is a major issue because AI data analytics doesn’t function properly when there is disorganized data. Think of it like cooking Nasi Lemak. You have rice, sambal, eggs, and cucumber. You cannot make the dish if you stored the rice in your car, sambal in the bathroom, and egg on your desk.
AI analytics automation requires the ingredients to be stored in one place (the kitchen) to produce results. This is why many small businesses struggle with AI driven business forecasting. They expect magic, yet they provide the AI with bad or garbage data. The solution is probably pretty simple: start small – find one data point to analyze. If you’re a café, track how many cups of coffee sold versus rainfall amounts on that day. When you discover a pattern (e.g., more rainfall correlates with increased delivery orders) you can then use sales forecasting AI tools to prepare your delivery drivers. You don’t need a major IT department. You just need to connect all the pieces together.
Is this just for big companies with big money?
A lot of people say this, “AI is only for big companies like Google or Tesla. Not for my small business (kedai runcit).” That may have been true five years ago but it’s not true anymore. Companies used to spend millions on enterprise predictive intelligence but now you can subscribe each month to have access to business trend predictions. There are even many predictive modelling solutions available today that come built into the software you already have, like your accounting software or your CRM system. You don’t need a data scientist; you simply need a change of mindset.
Instead of looking at the past to evaluate your business (what profit you made last month), begin to look ahead through your business (using AI to help you with this outlook). AI will help to give you the direction and clear path to drive your business by helping you avoid any potential traffic jams or periods of a smooth road. For business owners in Malaysia this means they will experience less stress on their pay day, they will know when to hire additional part time staff during holiday seasons and be able to sleep through the night without worrying if they will make their sales targets by the end of the month. Many companies such as BidaTech AI are trying to simplify this process, but the bottom line is this: The data is in your business today, you just need to ask the questions that will yield you the right answers.
Does AI predictive analytics work if my business is very small?
Small BusinessUnderstanding how AI scales down to fit a smaller budget and simpler operations.
