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RMC Recommend includes an extensive library containing prepackaged set of rules, configurations and machine learning algorithms to execute relevant and automated cross channel campaigns right out of the box. Create and manage relevant product recommendations with ease to truly personalise every message, every page, every ad and every interaction based on each customer's behaviour, preferences and purchase history. See Playbooks to discover a few of the 100+ configurations of how to see how you can use RMC Recommend to improve engagement, Average Order Value and conversion rates across the entire customer lifecycle.
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- Alternative – Recommend similar products or content in same category.
Similar products or content that are browsed together by visitors are tracked by the system and relevant recommendations are shown based on visitor's browse or purchase history, making it easier for customers to find alternative products they might otherwise not know about. If the algorithm fails to find similar items in the same category, it searches for categories that are viewed together the most and displays the most viewed products in that category. Enter your own rules to automatically promote higher ticker items, new brands or tags.
Sample Widgets: Viewed this also Viewed that, Viewed this and Bought that, You might also like (based on browse and purchase history), You might also like (with up-sell rules), You might also like (tag based - pushed)
- Complementary – Recommend complementary products in same or related categories.
Visitors are recommended products that are usually bought together to increase the AOV of each purchase. RMC allows you to easily to select the family, brand price points of products you want to offer alongside the main item that a customer is recommending.
Sample Widgets: Bought also bought, Frequently bought together, Related items, Complete the look, Featured outfits (User created product collections).
- Generic Recommendations w/ user behaviour
Generic product recommendations do not depend on any product as a baseline and stand on their own. They can be generated without any knowledge of user behaviour or preference. You can easily add customer cross channel behaviour, purchase and preference data to these recommendations to make them more meaningful and engaging.
Sample Widgets: Best sellers, Trending now, New arrivals, New markdowns, We recommend, Category best sellers, Recently viewed (based on browse history).
What is Playbooks?
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Why should I be using Recommend?
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- Unified data – RMC allows you to connect, collect and unify data from various sources. Integrate your off-platform data, such as POS or order management, and merge it with cross channel behavioural and profile data, including ratings, reviews and surveys, as well as additional marketing data, such as product catalog and coupon codes.
- Deep segmentation – Our Advanced Segmentation gives you the power to use customer data from email, web, mobile, social and offline channels - including cross channel behaviour, purchase history and customer interests. This complete view of your audience allows you to effectively segment, target and personalise your messages.
- Advanced merchandising –merchandising – Recommend allows you to easily apply your merchandising strategy into existing algorithms. Apply business rules and strategies to automatically and systematically promote higher priced items, product bundles, necessary add-ons, particular brands, products or categories.
- Personalised email solutions – Supercharge one of your most important marketing channels with personalised product recommendations and triggered campaigns.
- Orchestrated personalisation – You don't want to send an offer to a shopper who abandoned their cart online and since then completed the purchase in the store. RMC links customer data from online and offline channels, preventing repeat communications and allowing marketers to deliver the right message every time, everywhere.
- Continuous improvement – Experiment with different strategies using our built-in A/B Testing feature to find the right campaign for every stage of the customer lifecyle and optimise your results.
- Revenue analysis and attribution – Recommend allows you to access and analyse the revenue impact of recommendation campaigns across channels in real-time through a single Performance Dashboard. See the breakdown of revenue across channels and campaigns to optimise your marketing mix and budget.
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- Browse abandonment campaigns – Send known visitors who browse without adding anything in their cart an email to bring them back to your site to incentivise them to make a purchase. Recommend products and categories based on each visitors individual browsing history, and show recently viewed items (product or category) to motivate additional purchases.
- Cart abandonment campaigns – Encourage would-be customers to complete their purchase when they leave items in their shopping cart. Automatically send known visitors an email that includes the items they selected onsite and also recommend related items based on their selection to inspire further shopping.
- Post-purchase campaigns – Post-purchase emails provide a strong cross-sell opportunity. Include product recommendations triggered by the customer's purchase history to increase customer lifetime value.
- Replenishment campaigns – Trigger personalised replenishment emails based on each individual customer's purchasing cycle, and automatically remind them to repurchase products just before they run out. Increase customer lifetime value and increase your customer retention rates.
Enhance the Customer Experience Across Your Entire Web and Mobile Sites
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Here are a few examples of plug and play recommendations that our clients use across their web and mobile sites to aid product discovery, increase average order value and conversions:
- Home Page – Automatically showcase the most popular items across your store to inspire first time visitors with trending items as soon as they arrive. Or greet returning customers with products they have previously interacted with and shown an interest in, helping them to continue their shopping journey and complete the sale.
Recommendation typesSample Widgets: Trending now, Best sellers, Recently viewed, Recently viewed and featured recommendations. - Search Pages – Narrow down long search results by recommending the most relevant products according to the browsing and buying behaviour of customers who previously used the same search keyword to facilitate product discovery.
Recommendation typesSample Widgets: Searched and also viewed, Searched and also bought. - Product Pages – Recommend complimentary items to the one viewed on the product page, making it easier for visitors to find related items, making it easy for visitors to find related items and encouraging them to add more to their cart.
Recommendation typesSample Widgets: Frequently bought together, Related items, Complete the look. - Shopping Cart – Recommend products that other customers have typically purchased with the items in your shopper's cart, highlighting complementary products at this stage of the buyer's journey increase the chance to increase the order value.
Recommendation typesSample Widgets: Frequently bought together, Related items, Complete the look.
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