Clickstream Data Analysis for Real-time Upselling and Cross-selling

cross-sell up sell clickstream analytics

We are currently living in the digital era, wherein billion-dollar businesses are created and managed on the Internet. Let’s take e-commerce for instance; organizations such as Amazon and Walmart have established themselves as juggernauts in the field, with a net worth of $410 billion and $220 billion, respectively (source: CNNMoney). Needless to say, websites play a major role in propelling businesses by maximizing conversions and sales. However, that’s easier said than done because for every profitable website, a lot of analytics and optimization efforts are made behind the scenes on an ongoing basis. And all these efforts are made specifically for people who have the power to make or break businesses – the customers.

On any e-commerce site, a lot of exploration, clicking, and comparison shopping happens but with little sales and conversion. As per E-consultancy’s latest survey, on an average, a visitor spends 4-5 minutes browsing through e-commerce sites, but the online conversion rate still remains at 1.4%. To improve online conversion and sales, e-commerce majors like Amazon and Walmart personalize their website content and create offers to target various customer segments.

Fortunately, this can be done using clickstream data analysis with minimum investment, and one does not need to be as big as Amazon to accomplish this. Primarily from a data standpoint, it will require clickstream data, historical customer purchase data, and product data. From a tool perspective, any A/B testing tool like Google Experiments and Adobe Target with content-loading capacity can come to the rescue of the site operator.

Clickstream data can tell an e-commerce site owner what products the customer has been browsing, the product categories the visitor is exploring, and how prices, ratings, and other relevant information are influencing buying decisions.

Historical customer purchase data helps uncover past purchase information and product details, including price range and offer availed. This data also sheds light on customer credit information, reviews, likes, and interests of customers. Product data, on the other hand, gives information on what kind of product is selling from which categories, stock position, and affinity of products.

Creating Offers for Upsell or Cross-sell

The first step would be to apply analytics on customer purchase data and product data. For example, a market basket analysis can be done, which will give insights to the site owner about the affinity of products, like what products can be sold with the respective categories of products. This will help create a repository or database of products, which will be needed for the upsell or cross-sell offers.

The next step would be to analyze the clickstreams. For example, the data may show that a visitor has been clicking on high fashion garments in the $80-$120 price range. This clickstream data can be compared with historical customer data, which may show that the customer usually purchases high fashion garments around the $100 mark. It would also tell the customer preference to product ratings and last purchase data.

Meanwhile, using tools like Adobe Target or Google Analytics Experiments, customer segments can be created with customer IDs. And by leveraging the product database or repository, a connection can be put together in the tool. Now in real time, whenever a targeted customer (with a customer ID) visits, the site may then list high fashion garments that meet historical criteria plus an upsell offer from their product database: a $150 garment with a five-star rating that has been selling well across its site.

Combining clickstream data and historical data also creates opportunities for cross-sell, as retailers would know which products are bought together. This information can help make personalized offers to customers. This will also help to solve a lot of cart abandonment issues and win back sales by incentivizing customers with offers, coupons, and shipping rebates when they re-visit the site and are idle without adding anything to cart for a certain duration. Real-time clickstream and historical data integration can thus help e-commerce sites retarget, cross-sell, upsell, and increase conversions on the fly.

Clickstream data analysis and study of browsing behavior can facilitate many more business outcomes aside from improved conversions with upselling and cross-selling. Retailers can use these insights for optimizing digital marketing spend, improving customer journey and experience, and so on. Below are a few use cases along these lines.

Optimizing Customer Journey

A leading hotel based in London was looking to understand the behavior of its customers who were using its online portal for bookings. Also, it intended to optimize customer journey through clickstream data analysis and behavior study.

BRIDGEi2i helped the client:

  • Optimize customer journey across different websites based on the ‘browse to buy’ behavior analysis
  • Create a ‘single version of the truth’ of the company performance by building a dashboard aiding the business stakeholders with actionable insights

To know more, read the complete customer journey case study.

Optimizing Digital Marketing Spend

A leading Fortune 50 global information technology company, which manufactures and sells hardware like personal computers and printers via brick and mortar and online channels, was looking to optimize its digital marketing spend.

BRIDGEi2i helped the client identify SKU-specific target segments for digital banner campaigns. Also, it analyzed customers’ browsing and purchase behavior on digital channels and leveraged the derived insights to optimize marketing spend.

For all the details, read the complete digital marketing spend case study.

Improving Customer Experience

A leading US-based Fortune 50 technology company, which designs, manufactures, and markets mobile communication and media devices, was looking to improve customer experience on their support website.

BRIDGEi2i helped the client:

  • Increase intent completion rate for customers by 7%
  • Increase customer satisfaction of the support website visitors
  • Reduce customer support cost by reducing the number of calls to support centers

Read the full customer experience case study for more.