Internet trends: marketing research & predictions

5 predictions for socio-location recommendation behavior

September 10th, 2010 by

Google (and Yahoo) brought opportunities to the online retail. Location Based Services will bring promising opportunities to offline shopping.

Much has been said about recommendation sites and smart engines as Pandora, Netflix, Amazon and Google.
Looking back at the last ten years – recommendation engines started with item comparison. Personalized engines were then developed and offered suggestions (predictions) based on users past behavior, claimed preferences, or computer pre-defined identification systems.

When social parameters were added – users were exposed to other decisions made by anonymous shoppers (or popular search results).

Today, when social interactions are mainstream, and technology (smartphones adoption continue to rise ) enables location based services, we get new dimensions added to the equation.

According to the Social Comparison Theory people are especially prone to compare themselves to people they view as similar to them. Research has also shown a strong link between social comparison and peer communication about consumption.
Given a location system added to the social knowledge – users are exposed to practical and immediate choices.
Having a direct knowledge on friends buying decisions in times relevant to decision making will certainly influence decision making process. Acknowledging that, Facebook has established Places.

What will social networks and location based recommendations add to this eco-system of recommendation sites?

Following the entrance of location based networks as Foursquare and Gowalla, we have prepared a list of predictions and highlights for future research:

Prediction 1.
Multiple based recommendations might bring to consumer confusion:
Issues to be tested:
1. Will people be able to differentiate between location based recommendation (just because you are here) to a different recommendation type (their pass behavior for instance)?
2. Will people want to learn how to differentiate between parameters which influence their decision?
3. Assuming this given choice – will people really put efforts to chose their preferred recommendation parameter in real time?

Prediction 2:
Location will improve personal voting behavior if it will be connected to real benefits.
Issues to be tested:
What benefits will influence personal voting behavior? (checkout discounts, product giveaways etc) and what will be the preferred form of benefits (first to come, coupons, accumulate loyalty ..)

Prediction 3:
Social presence (quantity: amount of friends / people) will count as quality.
To be tested:

1. Assuming many of ones friends visited a place or purchased a product – would this replace reading their reviews?
2. Are all friends come equal? Will people differentiate between friends (work friends. network friends) as the reliable source of influence?

Prediction 4:
Offline offerings will be more dominant than online offerings with LBS entering the decision making process:

Entertainment (restaurants and bars, events) and offerings made by physical stores will lead the local revolution.
Issues to be tested:
What offline industry sectors will better fit the local recommendation behavior (entertainment? fashion? electronics?)

Prediction 5:
With LBS, local cultures will define consumer behavior.

Consumer learning will shift from demographics (traditional behavior) and digital networking (global influence) to local communities.

To learn more on experimental marketing activities of brands using socio-location  incentives- follow the reviews made by Click, Read Write Web and ABI Research.

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7 Responses

  1. StefanW (Stefan Wolpers) Says:

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  2. Web2ourist (Yossi Cohen) Says:

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  3. MMidas (Mario Midas) Says:

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  4. thinksync (thinksync) Says:

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  5. Serenitys_68 (Serenitys) Says:

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  6. Emarketed (Emarketed) Says:

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