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E-commerce: How personalized engagement helps to improve cart value at the checkout process?

Posted On November 9th 2020

E-commerce check out process

Back in the early days of e-commerce growth, increasing visitors on your website was considered a key metric followed by the measurement of the number of visitors vs buyers. One key drop-off point observed in the customer journey was at the checkout process. Many studies on cart abandonment revealed that customers end up not buying the products for many reasons, including high shipping cost, too many details required to checkout, no offers/coupons, lack of trust with card details, low buying intent, delivery days are too high, etc.

In all such scenarios, e-commerce portals saw high footfall in terms of visitors but were struggling to convert visitors into buyers. In effect, Average Revenue Per User (ARPU) was not increasing, despite the increase in website traffic, thereby impacting the sales forecasts. This warranted for e-commerce retailers to heavily focus on their checkout experience and strategies to reduce shopping cart abandonment. In later years, many solutions were implemented to reduce shopping cart abandonment and improve ARPU numbers. Few such solutions include transparent pricing with the shipping cost, guest checkout, minimal steps or single checkout page, coupon code right at the checkout page, providing product reassurance in the form of social reviews, ratings and so on. This significantly reduced the drop-off rates at the checkout process.

Today, e-commerce processes are much more matured on the checkout experience. Exclusive strategies are being implemented around abandoned cart recovery. Follow-up communications with the customer are being done in an attempt to understand the reason for abandoning and engage them better to close the sale. This has shifted the benchmark still further on solutions to improve cart values and conversions. It turned out to be that just line up of products on the website alone is not going to be enough; the users have to be engaged more and more to have a healthy conversion ratio. This engagement had to stretch beyond the website to include offline communications as well to recover abandoned carts.

The future: Customer engagement has become one of the key metrics for any business looking to sustain and grow in the long run. Industry data reveals that 80% of customers are more likely to do business with companies offering personalized experiences. With more users now shopping online than ever, advanced technologies such as Artificial Intelligence (AI) are helping e-retailers to understand and engage their customers better and faster. From automated chatbots to personalized email triggers and dynamic CTAs for amplifying “need” and “urgency”, AI systems have enabled e-commerce stores with better and personalized customer engagement. How much ever is done, there is still more to do! Along with improvised e-commerce experiences, customer expectations also grow exponentially. This mandates e-commerce players to be more innovative and implement advanced strategies around customer engagement; this is further amplified with an increasing number of competitors as well. There is still a wide gap between customer expectations and what e-commerce industry provides on the engagement and experience front. Industry data reveals that 80% of customers say the average retailers don’t understand them as an individual, resulting in poor customer engagement. Also, this directly reflects on conversions, i.e., visitors vs buyers. As per the industry average, only 25% of customers contribute to 66% of sales.

Every customer who lands into the shopping portal is an inbound lead for a retailer. If the shopping portal is not engaging the customer, the customer session ends there. Average order value drops there. The customer engagement should begin right from the landing page, immediately after the login gets over. One of the key strategies in engaging customers is to provide personalized product catalogue with items that are aligned to their interests readily displayed in a catalogue.

The personalized catalogue experience enables the e-commerce portal pages to reflect the behaviour of the in-store sales persona and engage customers with a display of products aligned to their interest. During the checkout process, personalized catalogue of discretionary items also increases cart value, just like how discretionary items are stacked at the in-store billing counter, for any last-minute impulsive purchases. More than the business value, the experience creates a strong impression for the buyer to re-visit the e-commerce portal again. Enabling the e-commerce platform with intelligence infrastructure at the backend would be a critical success factor on customer engagement.

How Datsy can help the e-retailers in customer engagement to reduce the cart abandonment rate more, thereby increasing sales?

Thinking of intelligence enabled backend, we should also be cognizant of the fact that AI systems are time, effort and cost-intensive. The total cost of ownership (TCO) of such systems are too high, making it a far-fetched dream for any mid or small online businesses. This is one of the major parameters which increases the competition gap between Tier1 and Tier2/3 e-commerce players.

Datsy Suggest provides AI-driven hyper-personalized recommendations for e-commerce applications on a pay-as-you-go SaaS model. This relieves Small and Medium businesses of any entry/exit overheads and liberates them to experiment on various engagement strategies through personalized product recommendations.

From landing page to checkout page and beyond, Datsy Suggest enables personalized recommendations across the visitor journey. This includes personalized menu bars, home page collections, product recommendations on product details, checkout page etc. Whenever the customer lands on the checkout/cart page, Datsy Suggest can help to present the customer with the best combo of products and other discretionary items aligned to the customer’s interest. The customer can add them to the cart right from the checkout page. Few examples like, “Suggest best belts that go with pants selected”, “New design t-shirts from your favourite brand” definitely grab customers attention and facilitate conversions. Relevant and useful recommendations personalized to customer’s interest will increase average order value by 10 to 30 per cent. Datsy Suggest can enable that for you!

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