Beyond purchase history

Retailers and online behavior

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Content DiscoveryBeyond retargeting and social

recommendation

Showing the same product (you already bought?) can fail.

Product recommendation is not just for retail sites.

Audience DataPersonalising all marketing and developing

offering with online purchase intent

Purchase histories fail for durable goods: If you just bought a bike, you’re not buying a

new one soon.

CONTENT DISCOVERY

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Retargeting: “You looked at this, you will see it again”Retargeting is better than no targeting, but is often annoying and ineffective. Showing the same product too many times turns customers off, and if they already bought it’s worse.

Social recommendation: “Users who bought this also bought..”Pioneered by Amazon and very useful. But only works within the retail site, not elsewhere.

Status quo in recommendation

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1. Contextual targeting across sites and content typesRecommending products or messages related to the content user is viewing. “You’re reading a bike review? Here are our offers on similar bikes”

2. Behavioral targeting across sites and content typesRecommending personally interesting products based on media viewing. “You read about climate change and watched a video of a car show?Here are our offers on hybrid and electric cars”

Other ways of recommendation

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Iltalehti.fi newspaper is a top 2 site in Finland.

On the front page Leiki shows personalised recommendations based on the user’s clicks throughout the media group’s network with Leiki SmartPersonal. This provides easy access to personally most interesting content.

Users see personal recommendations even on their first visit to the site, if they’ve clicked on any other media group site.

On the article pages we help the user to stay engaged with the whole media group with contextual recommendations from Leiki SmartContext.

Case: Tabloid newspaper sends traffic to premium content

Example: Car review with related content from various sites. Recommendations include articles from (1) within the site, (2) from other media group sites and (3) related car sale ads from online marketplaces.

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Case: Multi-sector Retail Group

User reads an article on wild mushroom hunting on customer magazine. This updates the personal interest profile.

On the first visit to department store site, the front page recommends products matching the user profile, such as a GPS navigator.

Contextual recommendations on the article page.

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What is needed?Semantic understanding of content and user interests

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Dynamic creatives for Pre-targeting

Automatically scan web stores to promote the most relevant items in dynamic creativesPre-target product offers in display ads both contextually and personallyEach product can be presented alone or in a rotating multi-product window

AUDIENCE DATA

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Purchase intent profileGenerated in real time from media browsing

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Define audiences around any interestNew types of segments that have been unavailable for publishers before, e.g.;• Interests on any product category• B2B-decision makers - heavy readers of

advanced economy/business news• Seasonal or temporary segments, such as Tyre-

Changers, Christmas shoppers, Eurovision Song Contest

Market insight reports that summarize semantic topics and trends on the campaign• Finding the right audience segments and topics

that most interests the advertiser’s customers.

Online audience targeting

ResultsInterest targeting consistently achieves a more than 200% increase in CTR over standard targeting.

SmartSegment: Gigs & EventsAdvertiser: Live Nation

SmartSegment: Beauty QueensAdvertiser: Nestle

SmartSegment: Video GamesAdvertiser: Retailer Gigantti

SmartSegment: Sports FanaticsAdvertiser: Betting Agency ComeOn

1,00% CTR

0,66% CTR

0,43% CTR

0,41% CTR

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Find interest differences between demographic groups

Drill deep into specific topics or see more general trend lines.

Compare the differences between visitors from different geographic areas or using different device types.

Discovering consumer interests

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Drilling down into brand preferences

What are the interest differences between consumers who choose iPhones or iPads and those who go with Android? Analysis with select fashion brands.

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Daily goodsSomebody who buys fat-free milk every week will probably buy it next week as well. Analyzing purchase histories works.

But what if they’ve been reading about oat milk recipes, but couldn’t find it in your store?

Beyond purchase histories

Durable goodsSomebody who just bought a bike is not going to buy a new one next week. Purchase history is not very useful.

Now they are reading office chair reviews. Who will market to them?

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How to integrate online behavior?

Consumer purchase intent integration into CRM1. Online interests are analyzed in detail, in real time

2. Online interests are mapped into your product taxonomy

3. Anonymous purchase intent data is sent to CRM

4. Anonymous cookie ID is sent to retailer on-site

5. Retailer maps anonymous ID to customer ID

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Intent dataanalyzer

CRM integration of online intent

Mapping into retail product taxonomy

Retailer site

Connection with Customer ID

Analysis of interestsfrom browsing

Product purchase intent transfer

Transfer of anonymous ID

MediaRetailer

CRM

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Using online behavior with CRMTargetingMake targeted offers of products related to consumers’ online interests. In all channels!

Development of offeringPredict changes in consumer behavior before they are realized in purchases. Develop your offering to match future needs of different demographics.Stay ahead of your competitors who are looking at the past!

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Using demographics with behavior

Start with interest, limit with demographicSelling expensive sportcars? Good idea to combine interest in them with high household income level.

Start with demographic, limit with interestsTire-changers segment: People who own a car, but interests indicate that they’d like someone else to change the tires. Such as busy parents with children practicing sports.

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Winning with online behavior

Offer what they want to buy before they’ve bought itSend a personal paper / mobile coupon of a product with highest online purchase intent to each loyal customer.

Find the product category with highest online intent and not yet realized purchases in each locality. Tailor local shop offering before the competition.

Find the product segments with highest consumer interest that are missing from your inventory – for each store.

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Roy McDonaldPresident Leiki NA+1 650 8676262roy.mcdonald@leiki.com

Thank You!

Dr. Petrus PennanenCEO & Founder+358 40 5020355petrus.pennanen@leiki.com

Martin SänttiBusiness Development+358 400 977553martin.santti@leiki.com

Jaakko HaaralaBusiness Development+358 40 1384795jaakko.haarala@leiki.com

Leiki HQHelsinki, Finland

Leiki NASan Mateo, CA

www.leiki.com