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Using the right data signals to boost eCommerce sales
eCommerce businesses today recognize that predictive analytics – a discipline that helps marketers use customer data to improve personalization, understand user behavior, and anticipate customer needs – has the power to drive significant business growth. In the present climate, specifically post-pandemic, historical data is insufficient predict customer behavior accurately. The pandemic has impacted most retail markets and forever changed the customer experience. Retailers and e-commerce companies must react quickly and incorporate new insights from data analytics in order to make more timely business decisions. As the accuracy of predictions made based on historical business data wanes, companies are quickly realizing the value of external data. Incorporating the right external data signals into predictive models leads to improved conversion rates, operational efficiency, and customer retention. This article will go over some of the new challenges of predictive modeling for eCommerce, some use cases, and how to get started.
Major Trends Driving the Need to Predict Customer Behavior in eCommerce
Before we get into “how” to predict consumer behavior, let’s go over why it has become so important. There are 2 major trends in eCommerce that are worth discussing:
1) eCommerce in general is booming. Especially after 2020 – the pandemic accelerated the adoption of eCommerce shopping. In fact, there has been massive digital adoption overall, across verticals and industries since the beginning of COVID-19.
2) There are a lot of new eCommerce customers from different age groups, including customers which had previously never shopped online prior to the pandemic. Many existing customers are looking for new brands, new experiences, and lower pricing. With an excessive amount of options now available to customers online, they are not as loyal as they used to be. Customer satisfaction is changing quickly and becoming more difficult to achieve.
The combination of increasing demand online, and decreasing brand loyalty as shoppers seek out better options, means that eCommerce companies need to adapt their customer acquisition and retention strategies.
Digital direct to consumer brands are at an advantage, since they started out online, and will have data available to them about the customer journey, how their customers’ behave, and their purchase history. eCommerce companies have useful data at their disposal. They need to figure out how to leverage it in order to reduce customer acquisition costs, attract higher value customers, and reduce customer churn. This is why there is an increase in eCommerce companies attempting to leverage predictive models. However, to get a competitive edge, organizations need more than the data that is available within their four walls. The most accurate predictive models require third party data in addition to what a company already has in terms of historical, internal data. Data expires – feeding models with limited historical data will not be effective on its own to predict future trends. Smart data leaders are looking for new data sources to get context, and understand what is happening in the market. This is key for eCommerce, as it is booming, competition has leveled up as well.
The New Challenges of Modeling Customer Behavior
Online ads are a common tactic for eCommerce businesses to acquire customers. These days, buying media is harder than ever. With the introduction of new privacy acts, such as the recent i0S14 privacy changes, it is more challenging to find the right users to target. Improving ad buying efficiency through artificial intelligence and data modeling has become key to improve results.
When it comes to leveraging artificial intelligence and machine learning, the process can be resource-intensive, and requires strong technical skills. It is typically an expensive initiative, especially for smaller companies.
In eCommerce, there are many potential use cases for machine learning models, such as lifetime value prediction, online funnel personalization, and product recommendation systems. These models exist in the industry yet are not super common outside of the larger eCommerce brands. It is hard for the smaller businesses to estimate what kind of value or impact a specific model will bring to their business.
In addition, finding, acquiring, and integrating relevant external data presents its own set of challenges:
- Today, there is a huge amount of big data sets and external data sources available. According to a survey we conducted in March 2021, over 79% of data leaders consider external data valuable for ongoing business efforts, yet 93% struggle to find external data that’s relevant to their needs. The amount of data available can make it harder to find the highest quality, or most relevant data.
- Procurement of external data is a long process. It takes an average of 3 to 5 months to find and onboard external datasets – causing many businesses to struggle with the data acquisition process.
- Buying datasets can be expensive, and typically, there is no way to gauge the impact of a dataset prior to purchasing it. All of this can lead to a high risk, low reward situation.
- Consuming and integrating external data into existing ML and BI models is difficult, as the data formats often don’t match with a company’s internal data. (Data scientists spend 80% of their time on data wrangling)
For companies restricted in data science personnel and resources, automation is the solution. They can make use of data science as a service or external data platforms.
Ad Retargeting – The GlassesUSA Story
GlassesUSA – an online retailer for prescription eyewear – used predictive modeling as part of their Facebook marketing program. They wanted to improve their ad remarketing efficiency in order to increase the conversion rates of their Facebook Dynamic ads. They created a ‘predict-to-buy’ model that categorized different types of users with the highest likelihood to convert. This model was used to create specific audiences on Facebook to target with remarketing ads, while identifying and excluding the audiences deemed least likely to purchase. Doing this increased ROAS (return on ad spend) by 10%. Correctly identifying the right audiences to target made the ads more efficient and more likely to hit the right users.
Using Explorium’s External Data Platform, not only enabled them to expedite the process of external data acquisition, but also enabled them to build an accurate predictive model, trained on relevant data. The platform made the whole process more efficient for them.
Consumer Lifetime Value Prediction
Big eCommerce companies are using customer lifetime value (LTV) prediction models to power their marketing strategies and marketing campaigns. To get accurate LTV predictions, they need to incorporate their own internal data, along with external data to train the models. The internal data that they use for these models includes transaction history on a user level, behavioral data (web/app sessions, clicks, page views, email open rates, call-center interaction data), and refund data. This data can be enriched with external datasets such as demographic data, social media data, and online behavior data beyond the company’s own website. By using a combination of internal and external features and signals, companies can build the most accurate predictive models delivering the highest impact.
How to Predict Customer Behavior – Getting Started
For eCommerce business, there are 4 key steps to consider prior to beginning predictive modeling initiatives:
- Machine learning predictive model initiatives require commitment from the entire leadership team of the company.
- Organize and understand your data to get an idea of what you have, and what is missing. It is essential to understand what data you don’t have prior to the data acquisition process.
- Determine whether you have the resources to build the models with your in-house data science team, or if you will make use of an External Data Platform like Explorium’s.
- Make sure that you can connect the model outputs to your marketing platforms (such as ad platforms, and back-end marketing and product platforms). Otherwise, you will go through the work of building the models without experiencing the benefits in the end.
The ability to predict customer behavior is essential in scaling eCommerce initiatives and growing sales. The current landscape presents a number of challenges, but there are tools like External Data Platforms to help you along the way. To learn more about finding the right data to fuel accurate predictive models, watch our most recent webinar.
Explorium provides the first External Data Platform to improve Analytics and Machine Learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail and e-commerce can increase revenue, streamline operations and reduce risks. Learn more at www.explorium.ai.