Businesses relying more on data-driven decision-making perform better in terms of profitability. Despite its proven positive impacts and necessity in a digital age, few established a system truly integrating data into decision-making.
Back when data was exclusive to giant companies, SMBs had a small chance to survive. Luckily, big data becomes growingly democratized each day. No matter the size of your business, you can acquire data in meaningful volumes. What about interpreting and making use of it?
That’s the tricky part. How will you interpret big data and integrate it into decision-making?
We’re going to talk about why and how of it, but before jumping into that, let’s learn what makes data ‘big’?
What makes data big
Big data is high in volume, variety (referring to different types of data), and velocity, regarding the speed at which it is generated. It’s insightful because of its characteristics, which also make it harder to obtain and interpret.
Even so, high-volume data is not a privilege of retail giants anymore since it became a commercial asset. It used to be Amazon that personalized product recommendation, or Walmart that optimized pricing.
Today, SMBs can obtain data from data providers, social media, or competitive intelligence solutions. E-commerce SaaS companies offer data-driven automation and optimization solutions that compensate for the competitive advantage of retail giants.
73% of companies say that they use these external data sources, while only 11% generate it internally. No matter how you gather it, integrating data into your business decisions levels the playing field between SMBs and retail giants.
So, why wouldn’t you leverage the power of data? Here’s how you can benefit from data-driven decision-making.
Modern consumers suffer from a burning desire for personalization.
Despite the fact that we fear our privacy being invaded, the desire for a perfectly customized shopping experience outweighs our security concerns.
40% of executives in a Forbes survey stated that personalization has a direct impact on sales, basket size, and profits. But how do they personalize the shopping experience on their website?
Personalization from e-mail marketing to website design, from website navigation to discounts. All aspects of user experience should be personalized.
I bet you already know that product recommendations must be tailored based on personal shopping history and real-time data analytics. Likely, you haven’t heard of personalized product descriptions.
Generating product descriptions is a time-consuming yet critical task for online retailers. They need to be consumer and SEO friendly, informative, and unique.
Recent research combined neural network and knowledge base technologies into a model that generates product descriptions meeting all the requirements above. But more importantly, they are personalized. The research is supported by the Alibaba Group, and the model is deployed onto one of its online stores, Taobao.
Of course, it might take a while before you can get your hands on this technology, but you must invest in similar technologies that are more affordable.
Creative yet easy-to-apply personalization ideas can add value to your business. We all know about geographical segmentation, but what if we could take it to the next level?
Local weather information gives you a clue of shoppers’ moods living in that area. Recommend cozy blankets or smooth jazz records on a rainy day and crop tops on a sunny one.
See if that works for your audience.
According to Harvard Business School Professor Alberto Cavallo, Amazon changed the nature of competition. As a result, prices are changing more frequently than ever.
The competition is so harsh that offering a seamless shopping experience is not enough to attract customers. They expect it at the best price.
That’s why Amazon’s in-house pricing engine monitors competitor prices and alerts the pricing leaders every 15 seconds, increasing sales by 35%.
But how can SMBs compete against retail giants like Amazon, Walmart, or Macy’s?
Surely by leveraging the power of data.
Price tracking software collects, stores, organizes and visualizes competitor price data, which must be utilized for short and long term decision making.
For starters, it allows you to control your positioning in the market.
If you want to be cheapest on popular products to drive traffic into your store (which is exactly what Amazon does), make use of dynamic pricing software. Even if competitors lower their prices in the middle of the night, the software automatically adjusts your prices against theirs.
Being cheapest will grant you a competitive advantage for a while, but you also need long-term planning.
That’s where big data comes into play. The software stores competitors’ historical price information that is revealing patterns in their behavior.
- Everybody discounts on holiday seasons, but what about their off-season behavior?
- Or, which competitors offer the best prices on a particular product/brand/category?
- On which popular products the retail giants compete at, and which ones they don’t?
Analyze historical pricing data to reveal patterns in competitor behavior, define their strengths and weaknesses, find loopholes that you can leverage.
You work with multiple companies in your supply chain and tracking the status of their operations is a difficult task. The bad (and old) news is, customers expect to track it too.
And they also want to see an expected delivery time/date. Luckily, supply chain management tools come to your help.
Supply chain management platforms help collect data from multiple resources, analyze big data and derive an estimated delivery date for the customers.
To prevent costly mistakes that could occur during transportation, AI tools determine the unmarked and impassable roads for route planners.
But for the hundredth time, short-term planning doesn’t bring long-term success. That’s why we integrate big data into long-term strategic planning.
These solutions generate digital representations of a business’s assets for safer operations and reduced costs. Sometimes with the help of 3D modeling, they foresee the possible problems in the supply chain and recommend maintenance when necessary.
It’s long past time to wait until a problem occurs. Integrate these data-driven solutions into your supply chain system to determine your costs beforehand, find out possible bottlenecks and take the precautions necessary.
Data-driven Cross selling
Cross selling involves suggesting additional products or services that complement a customer’s purchase history or preferences. Through the analysis of customer data, it is possible to identify frequently paired items and utilize this information to recommend further products or services that customers may find appealing. By integrating your data analysis system with your cross selling processes, you can better identify cross-selling opportunities and tailor your approach to each customer’s unique needs. To learn more about cross-selling and how it can benefit your business, check out our What is cross selling and how to use it to increase your sale article.
SMBs now have the chance to step out of giants’ shadow and thrive in the e-commerce industry with the help of affordable data-driven solutions that level the playing field.
Integrate these solutions into your short and long-term decision making to gain competitive strength and preserve it in the long term.