Tag Archives: touchpoints

IKEA’s digital flea-market – increasing the no of touchpoints

Just a quick share. Ikea’s second hand campaign reaches customers on various levels:

1) Ikea displays to be in line with the growing sustainability notion of environmentally friendly products. In Ikea’s case through prolonging the products life cycle and thus reducing the immediate disposal. I am not going to start a discussion on the real sustainability of this; after all, environmental management is requires more than an extension of a product’s life to create measurable bottom line effects.

2) Increasing the number of touch points with existing costumers. Whilst the disposal of furniture is often a major hustle for customers; it has suddenly been made easier with the help of IKEA. By allowing customers to display their old furniture on IKEA web properties. The effect is manifold, customers get yet again in touch with the IKEA brand and receive a positive stimuli to purchase yet again IKEA but also, additional touch points are created with new customers to introduce them to the IKEA brand.

Tagged , , , , , ,

big is great but smart is better – a big data discussion

I have recently had the chance to listen to a presentation by Phil Winters at the CRM Expo in Stuttgart. Phil is an active advocate of big data theories and a very lively presenter (can only recommend to sit in if you have the chance). Anyway, one of Phil’s slides caught my attention particularly.  A slide about a very basic but yet, as it seems, mostly ignored principle about big data.

IF YOU CANNOT MAKE SENSE OF IT – WHAT IS IT WORTH TO YOU?

In other words, BIG DATA – NON-IDENTIFIABLE DATA SOURCES = SMART DATA. The grid used to present this is about as easy as it gets but holds all the value a smart marketer needs to step into the big data discussion.

1) Visualise your customers purchasing decision making process (or the funnel if you want)

2) Identify touchpoints (this alone is a great exercise for most marketers and even more for internal service providers – customer centricity is the key – not what you want)

3) Assess data availability per touchpoint (is data readily available, in which form, when, from whom etc)

4) Assess smart data options (can you make sense of the data or identify user groups or even single users out of a specific data set)

5) Identify the data creator (is it a customer, potential customer, noise etc)

6) Smart Data entry (can you make sense of underlying values, behaviours or motives – in other words, can you interpret the data gathered at this level)

More from Phil Winters here – enjoy the read and happy smart data mining (I am a big advocate of logical naming conventions and from that point of view, big data is a misleading term, we don’t need big data but smart data; think about it!)

Tagged , , , ,