DeTweetify

There's no such word but I'm making a prediction that in 2010, someone somewhere will coin and use this word. huh! I love web 2.0. I can write whatever I want and get away with it too!!!

Here's my take on what DeTweetify will come to mean : DeTweetify will be defined on the lines of DeSeasonalize. Since Twitter activity sometimes produces spikes in sales/reputation/etc., you'd want to deseasonlize (or detweetify) your data to reveal the underlying trend!

Btw, it just so happens that a homonym 'DeTweet' does exist - see below (also see Urban Dictionary for similar fancy words)
Detweet

On Twitter, the passing along the tweet of another with some degree of disapproval. It can range from strong (that’s a lie) to mild (there are exceptions or conditions). It shouldn’t be taken as impolite or unfriendly - it’s done in the spirit of twitterville - as a new way to participate in the conversation. Done by adding 'DeTweet' or 'DT' before repeated post.
I thought his tweet was untrue, so I'm DeTweeting it to let others know they shouldn't believe it.


The new paradigm of customer decision-making process

The customer decision making process for purchase as taught in b-schools (Remember Consumer Behavior 101!! ):




The real life new paradigm of customer decision making process for purchase:



Source - KD Paine

Multivariate Testing and Blue Elephants

Here's how wikipedia describes Multivariate Testing:

"Multivariate testing is usually employed in order to ascertain which content or creative variation produces the best improvement in the defined goals of a website, whether that be user registrations or successful completion of a checkout process (that is, conversion rate). Dramatic increases can be seen through testing different copy text, form layouts and even landing page images and background colours."

The RDE framework as described in the book "Selling Blue Elephants" by Howard Moskowitz is:

"RDE is a systematized solution-oriented business process of experimentation that designs, tests, and modifies alternative ideas, packages, products, or services in a disciplined way so that the developer and marketer discover what appeals to the customer, even if the customer can’t articulate the need, much less the solution!"

So you see the connection now!


After listening to what Malcolm Gladwell has to say about Howard Moskowitz in this video....I couldn't resist buying the book and neither would you!!

Collaborative Innovation Optimization

{I wrote this for a term-paper - so, please excuse me for the pedantic tone.}
The growth of internet and increasing usage of social web for sharing knowledge has completely changed the way people view the role of collaboration in business and life. Of late collaborative culture has also started playing a pivotal role in innovation. Online collaborative tools have not only empowered internal resources of a company to collaborate more efficiently but also made it possible for companies to harness the creativity of outside world for innovation.

To utilize the power of collaborative innovation, many firms have implemented internal and external online collaborative networks without understanding how these initiatives align with their business goals. The key is to understand that different kinds of collaboration models suit different needs. Two important aspects of a collaborative network are governance and participation. Governance can be hierarchical (H) or flat (F) and participation can be open (O) or closed (C).  A 4-model collaborative innovation framework proposed by Pisano and Verganti in an HBR article (link1) can be very helpful for firms looking for guidance in this area. A firm can use this framework to identify suitable collaboration channels that align with its business objectives. Depending on a firm’s expertise in knowledge domain, ability to pick up experts, willingness to share intellectual property etc., one of the four models from the framework can be applied. The 4 models are – Elite circle (H+C), Innovation Mall (H+O), Innovation Community (F+O) and Consortium (F+C).


One of these models, Innovation Community is especially interesting. It represents open-flat network where anybody can propose problems, offer solutions and decide which solutions to use. This is the model that gives the maximum power and freedom to firms in harnessing ideas from anywhere in the world. However, because of its free-for-all structure, it has the potential to generate massive volume of data and is expensive to manage. It’ll be more difficult to extract useful ideas from innovation communities in near future when the amount of data on internet starts doubling every 72 hours (link2). This trend has far reaching implications on the future of collaborative innovation since the data generated by collaborative web will constitute a large part of this massive volume of data. One of the implications is the emergence of content curators (link3), who expertise in facilitating collaborative innovation.


Looking at BIG’s (Big Idea Group) business model in this light, we see that BIG, in some ways, is actually a content curator for collaborative innovation. It curates data from people who have ideas to share but do not have skills to make their ideas presentable and maybe do not know how to connect with firms that might find commercial value for their ideas. It’s not that the firms looking for ideas are not available on collaborative web to network with individuals. It’s just that removing noise and getting to valuable innovative ideas on collaborative web is itself a difficult task. BIG has positioned itself as an expert in this field. The expertise to extract innovative ideas from tetrabytes of data is an industry in itself and BIG is uniquely positioned in this space. Additionally, BIG’s 6 step process of taking an idea from its draft version to concept selling involves collaboration with idea owner at each step - perfect collaborative innovation in action.


I think one of the most challenging tasks in making collaborative innovation successful is finding creative ways of attracting and motivating right set of participants. We need to evolve a set of guidelines for “Collaborative Innovation Optimization” something on the lines of “Social Media Optimization” (link4 - Rohit Bhargava’s blog). Taking a cue from Rohit’s guidelines for SMO, a few items for CIO could be:
  1. Provide mechanism for different levels of collaboration (for instance people who do not have time for writing elaborate blogs do participate on micro-blogging and tagging sites; collaborative innovation networks can benefit from similar approach)
  2. Evolve structure as the network grows. (It makes sense to have an open-flat innovation community initially to generate traction but this same strategy could be detrimental in retaining experts when the community grows. Network should adapt as the volume grows)
  3. Get communities connected (make it easy for participants to leverage their contents across different innovation communities and help them build their individual microbrands)

Disruptive Innovation


{I wrote this for a term-paper - so, please excuse me for the pedantic tone.}

It’s quite intuitive to believe that established companies with a wealth of resources and talent are better equipped to innovate. However, we have all seen how successful companies – who in many cases also constantly add innovative features to their products – fail miserably while new start-ups take over. How do we explain this apparent paradox? Professor Christensen of HBS in  his book "Innovator's Dilemma" explains this paradox by providing a very insightful distinction between the different kinds of innovations that established companies and newcomers engage in. Sustaining innovations (incremental) are pioneered by established companies while disruptive ones (radical) by newcomers.




Apparently, the management principles that make a company successful in the first place are also responsible for preventing it from engaging in disruptive innovation. Whether it’s “customer-driven enhancements” or “ignorance of smaller and non-existent markets” or “blind focus on core competencies” or “overshooting” – they all create a culture that’s not conducive to disruptive innovation. On the other hand, a start-up does not suffer from any of these. It focuses on niche areas and tries to capture a small or non-existent market by experimenting with new ideas; all necessary inputs for disruptive innovation.

Even though, the recommendations to manage disruptive innovations offered by Professor C make good sense and I’m sure would help established companies deal with disruptive innovations in more creative ways, I still believe that the reason why they cannot compete with start-ups is not just strategy or the lack of it. I’ll try to explain my reasoning using an analogy:

Winning the disruptive innovation game is a lot like winning a jackpot lottery. You can marginally increase your chances of winning by buying several tickets but because of the sheer number of lottery tickets that are sold…it’s almost impossible to predict who’ll win. Of course someone will win and most probably an unknown man (start-up) from Silicon Valley. However, when we compare him with this rich guy (established company) who bought 200 tickets and still won only 10 dollars, we tend to ignore the fact that there are 5 hundred thousand other people (start-ups) who couldn’t make the cut. The sure shot way for an established company to win would be to buy all lottery tickets but that wouldn’t make much economic sense and so there’s not much they can do unless of course Professor C comes up with a statistically insightful way of winning a jackpot!


I think Oracle and Cisco are two companies that have safely guarded their territory by in- house R&D and aggressively eating the smaller fishes. They are always on top of things. Anyone who does anything remotely related to their areas gets acquired soon. I’m not sure how easy it is for other companies to follow these guys but they really seem to know how to manage (and capture!!) innovation in their respective fields.


Frequency Capping 2

The Atlas study reveals 2 very important aspects of ad exposure optimization:

1. There is no one-size-fits-all model for frequency capping. So, the advertisers who have been using 3-exposure-in-short-time mantra have been losing on either conversion volume or return on marketing investment.

The long tail of conversion frequencies













2. Most efficient frequency may not be the most profitable frequency. 

Freq.
Cumulative Conversion
CPA
1
25%
$29.58
2
40%
$33.56
3
52%
$37.40
4
60%
$41.22
5
67%
$44.21
6
71%
$47.34
7
75%
$49.89
8
79%
$52.19
9
81%
$54.24
10
84%
$56.06
11
86%
$57.56
12
88%
$58.83
13
90%
$60.17
14
91%
$61.31
15
92%
$62.26



Frequency Capping

For several decades advertisers concentrated their spending on commercials with the goal of reaching at least 3 ad exposures within short time periods (referred to as a frequency strategy). The inspiration behind 3-exposures-in-short-time came from a psychological theory proposed by Herbert Krugman in 1965 who suggested that the first impression of a an unfamiliar product or brand may still be confusing to the audience. Only the second impression can bring clarity and be noticed in order to have a positive effect on purchase behavior. However, starting with the 3rd or 4th impression already, the additional impact may be diminished or 0. The very smart ROI driven marketers didn't waste much time in figuring out that 3 was their lucky (optimum!)  number.

However, Atlas (now a part of Microsoft) found a flaw in the theory while conducting a study in 2003. Their study report "Optimal Frequency - the impact of frequency on conversion rate" is available here for download. I'll write more about this study in my next post. Atlas claims that it has been able to leverage its excellent capabilities in "frequency capping" for gaining competitive advantage - that explains how important "frequency capping" is for digital advertising.