Archive for category Intelligent Analytics Leadership
This is a good article from Business Insider to spur on thoughts about how to ensure you can do digital ROI. Key messages: Plan your data strategy and know how to properly model your attribution. Last touch won’t cut it. Link online and offline. Digital Marketers Got This Wrong About ROI. http://ow.ly/loky3.
The ideas in this article are related to ones in http://allthingsd.com/20130524/why-tumblr-was-a-massive-steal-for-yahoo/which really focus on what makes an online service more valuable and viable in the long run. Viable online strategies are ones that take advantage of the insights in this article by Adam Rifkin, CEO, PandaWhale.
“The big data process is built on the pillars of measurement, experiment, analyze and replicate, contends Brynjolfsson. The continuous implementation of that cycle based on real-time data will strand less data-oriented competitors.”
In a fast moving competitive world, “stranding” a competitor means winning the prize.
Connect with your customers. This article extends a theme that has become even more important as we need to prioritize Big Data efforts. Three “missed” opportunities are detailed in the article by TMCnet (http://buff.ly/ZfYllU). These opportunities include 1) connecting with customers immediately, 2) providing customized opportunities and 3) assertively creating new data. These are areas almost all companies should leverage more.
AbsolutData: Missed Opportunities in Big Data CRM
March 20, 2013
Three Big Data Opportunities Your CRM System is Missing
Social media and the Internet deliver data that offers added understanding to engage one another in real time, leading to richer emotional connections with customers. Although many customer relationship management practices involve mining big data, most regularly overlook powerful opportunities that can improve customer relationships and loyalty, and increase revenue.
The three most overlooked opportunities in big data CRM stem from lack of interaction on three different levels:
1. Connecting with Customers Immediately
Social media allows brands to see what customers are talking about in real-time. Consequently, one of the biggest missed opportunities in CRM comes from brands’ inability to act simultaneously with customers. Many companies are managed so tightly, they can’t respond fast enough.
Providing a structure for timely, relevant interactions and conversations with customers allows brands to use CRM systems more effectively. Algorithms and flexible plans for responding can help brands thrive in real-time interaction.
2. Providing customized opportunities – “These guys really ‘get’ me”
Big data allows us to listen and understand customers on a personal level. However, a missed opportunity comes from our inability to provide customized engagement to forge stronger, personal relationships.
While many brands are well-equipped to scan the airwaves for concerns or issues, and respond accordingly, they’re missing a crucial piece of the pie: the chance to engage customers already satisfied with the brand on a personal level. Brands need to have listening systems in place to engage with happy customers too. By offering customized rewards and opportunities, customers feel valued, and consequently, more attached to the brand.
3. Assertively creating new data
Brands typically take a passive role in measurement. Websites or other typical touchpoints are designed generically from a process standpoint and typically not from an information purposing standpoint. When developing a presence on new platforms or even re-examining their existing ones, brands should set up measurements for engagement ahead of time with an eye toward shaping the kind of resulting information and feedback.
The saying, ‘build your network before you need it,’ applies to social media and customer relationships just as much as anything else. Purposeful measurement of interaction is an opportunity many brands can further leverage in big data management.
Although big data can be intimidating, it drives real business through smart CRM initiatives. It can drive even more value when its unique characteristics are leveraged. With the right plan, skills and technology in place, companies can apply real-time data to their CRM practices and build stronger bonds with customers – closing the gap on missed opportunities.
Chris Diener is senior vice president of analytics at AbsolutData, a global analytics and research firm based in San Francisco.
Edited by Braden Becker
This is an article that came from a conversation that I had with Jeff Bertolucci (@jbertolucci) from InformationWeek recently. It highlights the value of thinking in terms of layers of applications instead of entirely new domains when we consider a Big Data strategy for our companies. We do a lot of good work in CRM analytics — incorporating Big Data in a way that takes advantage of it’s unique attributes is a way to add value to our existing applications and services. The same can be said for other areas of analytics within the organization. Knowing how to layer it in to an existing service structure can be a successful path for leading a Big Data strategy.
3 Big Data Opportunities For CRM Strategy
Jeff Bertolucci | February 04, 2013
Customer relationship management practices often don’t exploit big data effectively, analytics executive says. Are you overlooking these 3 opportunities?More Sharing Servicesshare
Big data can enhance your company’s customer relationship management (CRM) strategy, resulting in greater customer loyalty and increased sales. But according to one marketing analytics expert, many businesses’ CRM practices don’t use big data wisely.
In a phone interview with InformationWeek, Chris Diener, senior VP of analytics for AbsolutData, an analytics and research firm based in Alameda, Calif., described three of the biggest missed opportunities in big data CRM.
“Big data is somewhat of an intimidating topic to folks,” said Diener. “For a lot of people, especially in the CRM area, it’s potentially threatening,” in part because its area of expertise beyond their comfort zone. ….
If you are in market research (MR), you may debate the which is tortoise and which is hare, but you probably do see a race and competition for the prize. Or, more realistically, you may fear that others see it as a competition. The prize may be budget, recognition as the source of valuable customer insights, or winning jobs. This may be a limiting perspective. If you are in the Big Data space could see it as a race but MR is probably not on your radar or you consider the outcome a foregone conclusion. You probably believe that MR could be a valuable appendage but ultimately discount MR when viewing the main role and impact of Big Data. There may be some blind spots. However, the two areas do overlap and treating it emotionally or functionally as a competitive situation will be counter-productive and lead to lost opportunity.
Some say that focus group and survey-based market research has reached the end of its life-cycle and will be replaced by Big Data analytics. Those who fully promote this position usually have an agenda to push or they are not well informed about market research. However, it does seem that these two areas of market intelligence, customer insight, and decision support both offer valuable and maybe even critical decision support. The million, or billion, dollar question is how to integrate them and leverage them together. Will this integration be the basis for the next round of killer market research applications or the means of getting the most value from your big data investments? My thoughts in this area were triggered by the linked article that contains some comments, including one application that combines the two areas of MR and Big Data.
In both areas, those who wish to capitalize the most will figure out how to combine them to leverage value. In Big Data, folks will come to realize that there are many holes in the data, many missing links, many areas where current or past data will not suffice in predicting the future, and many areas where the value of the insights can be magnified through the leaven of custom MR.
On the other hand, MR folks either have or will come to recognize that the in-context reality and pure organic validity of Big Data adds immensely to the precision inquiry possible in MR. Big Data integration will increase MR value by bridge limitations or biases created by interviewer/researcher subjectivity, sampling frames, respondent behavioral self-report inaccuracies, perceived insights credibility, perception of frequent lack of tangible real-world action-ability, to name a few.
Seeing or specifically identifying the areas of combined value will be the first and most important step to making this happen. The next step, and arguably the more difficult one, is to actually combine the functions related to Big Data and MR to bring about a profitable union. Most corporations have these functions silo-ed separately, based on their historical origins and historically separate functions, skill-sets and processes. In fact it goes even deeper in that these two areas may compete for the same budget and in fact may feel quite competitive with each other for recognition in the organization. Executives not only have an organizational issue to resolve but likely an emotional one as well.
On the supplier side the difficulties are multiple for being able to combine these areas. First is that most MR firms do not have an understanding of or a captive skill set that encompasses Big Data analytics. They don’t have the mindset for what it is, how to get it, and what to do with it. Next, most emerging Big Data firms come from a programming, IT, data mining, or highly technical area of strength. They do not have the insights background or training and skills. The foundations of marketing strategy and consumer behavior and psychology is lighter than it needs to be. The softer, more granular or individually based understanding of motivations, attitudes, drivers, etc. and how they mix together to generate action and commitment are usually missing with folks who have been devoted to algorithmic programming or econometric modeling and mining.
The future in this area belongs to people and organization who can bridge these domains. This is a process and the sooner it begins the better and the more likely you can capitalize on it for competitive advantage. Identify the needs, find the relevant gaps in addressing the needs, prioritize and act on filling the holes and showing tangible progress through a series of quick wins.
To motivate these changes, we need to see more examples of how the two domains combine for added value. The referenced article (noted below) provides one lightly sprinkled example. I believe we will begin seeing more.
How will it play out? Will Big Data analytics be the hare? Will the hare win? Maybe fundamentally, is there a race? Maybe, we see the races either being set up or already in process and the question is how do we change the nature of the race so that instead of competitors, the tortoise and the hare are teammates?
Referenced article: http://smartdatacollective.com/sandrosaitta/94181/guest-post-mark-zielinski
“The general consensus seems to be that “big data” is never going to replace traditional research – that is, specific research methods like surveys and focus groups that deal with particular topics will always be around. These specific research methods answer the question of “what” – that is, they are concerned with empirical details. For example, an online survey may indicate that compared to 2011, this year 5% of soda drinkers no longer drink Coca-Cola on a regular basis. Where “big data” aims to change research is in the “why” – that is, the broad trends and underlying reasons why certain results have been obtained. Using our previous example, “big data” may be able to tell us that key nutritional influencers have recently been saying that carbonated sugary drinks reduce life expectancy by an average of 4 years in healthy individuals. By having both the “what” results and the “why” results, researchers can use this combination of data to have a much clearer picture of a particular situation, and potentially be able to advise their clients on how to act to obtain the results they wish to achieve.”
Visionary but Naive: We don’t need more data scientists — just make big data easier to use — Tech News and Analysis
Simple Big Data Framework but Main Article Perhaps Naive: “We don’t need more data scientists — just make big data easier to use” — Tech News and Analysis
As we try to break down the big task of tackling a Big Data strategy, having a framework greatly facilitates the process. A few blog posts back, I outlined one framework to use for attacking this area. This article contains another structure, somewhat unique. However, the author is not really applying the structure to this issue but rather as a way to support the argument for more software and less data scientists.
The article borders between visionary and naive. I agree that its inevitable that applications will be built to address more and more specific data use cases. However, the evidence is overwhelming that in the short run it is nearly negligent to advocate fewer data scientists or a smaller role for them. We are at the early stage of the Big Data cycle where data and the application contexts are ill defined. This is a period where grand promises can be made and then will likely be broken once implementation needs to happen in a justifiable way.
This does not mean that every company needs to hire their own data scientist — but rather that they at least have access to those kinds of skills in a consulting format. The talent is scarce for hiring but not as scarce for hiring as a consultant. This probably is the best approach for most firms at this stage.
- Dec 22, 2012 – 12:00PM PT
- By Scott Brave, Baynote
- 29 Comments
Sure, more data scientists would be great. But Scott Brave, of Baynote, says the better solution is to create analytics products that are so easy to use that you don’t even need a data scientist.
photo: Sergey Nivens/Shutterstock.com
Virtually any article today about big data inevitably turns to the notion that the country is suffering from a crucial shortage of data scientists. A much-talked-about 2011 McKinsey & Co. survey pointed out that many organizations lack both the skilled personnel needed to mine big data for insights and the structures and incentives required to use big data to make informed decisions and act on them.
What seems to be missing from all of these discussions, though, is a dialogue about how to steer around this bottleneck and make big data directly accessible to business leaders. We have done it before in the software industry, and we can do it again.
To accomplish this goal, it’s helpful to understand the data scientist’s role in big data. Currently, big data is a melting pot of distributed data architectures and tools like Hadoop, NoSQL, Hive and R. In this highly technical environment, data scientists serve as the gatekeepers and mediators between these systems and the people who run the business – the domain experts.
While difficult to generalize, there are three main roles served by the data scientist: data architecture, machine learning, and analytics. While these roles are important, the fact is that not every company actually needs a highly specialized data team of the sort you’d find at Google or Facebook. The solution then lies in creating fit-to-purpose products and solutions that abstract away as much of the technical complexity as possible, so that the power of big data can be put into the hands of business users.
By way of example, think back to the web content management revolution at the turn of the century. Websites were all the rage, but the domain experts were continually banging their heads against the wall – we had an IT bottleneck. Every new piece of content had to be scheduled and sometimes hard-coded by the IT elite. So how was it resolved? We generalized and abstracted the basic needs into web content management systems and made them easy for non-techies to use. As long as you didn’t need anything too crazy, the problem was solved easily, and the bottleneck averted.
Let’s dig a little deeper into the three main roles of today’s data scientist, using online commerce as a backdrop.
The key to reducing complexity is to limit scope. Nearly every ecommerce business is interested in capturing user behavior – engagements, purchases, offline transactions and social data – and almost every one of them has a catalog and customer profiles.
Limiting scope to this basic functionality would allow us to create templates for the standard data inputs, making both data capture and connecting the pipes much simpler. We’d also need to find meaningful ways to package the different data architectures and tools, which currently include Hadoop, Hbase, Hive, Pig, Cassandra and Mahout. These packages should be fit for purpose. It comes down to the 80/20 rule: 80 percent of big data use cases (which is all most ecommerce businesses need), can be achieved with 20 percent of the effort and technology.
Surely we need data scientists in machine learning, right? Well, if you have very customized needs, perhaps. But most of the standard challenges that require big data, like recommendation engines and personalization systems, can be abstracted out. For example, a large part of the job of a data scientist is crafting “features,” which are meaningful combinations of input data that make machine learning effective. As much as we’d like to think that all data scientists have to do is plug data into the machine and hit “go,” the reality is people need to help the machine by giving it useful ways of looking at the world.
On a per domain basis, however, feature creation could be templatized, too. Every commerce site has a notion of buy flow and user segmentation, for example. What if domain experts could directly encode their ideas and representations of their domains into the system, bypassing the data scientists as middleman and translator?
It’s never easy to automatically surface the most valuable insights from data. There are ways to provide domain-specific lenses, however, that allow business experts to experiment – much like a data scientist. This seems to be the easiest problem to solve, as there are a variety of domain-specific analytics products already on the market.
But these products are still more constrained and less accessible to domain experts than they could be. There is definitely room for a friendlier interface. We also need to take into consideration how the machine learns from the results that analytics deliver. This is the critical feedback loop, and business experts want to provide modifications into that loop. This is another opportunity to provide a templatized interface.
As we learned in the CMS space, these solutions won’t solve every problem every time. But applying a technology solution to the broader set of data issues will relieve the data scientist bottleneck. Once domain experts are able to work directly with machine learning systems, we may enter a new age of big data where we learn from each other. Maybe then, big data will actually solve more problems than it creates.
Scott Brave is co-founder and CTO of Baynote, an e-tail and e-commerce advisory business. He is also an editor of the “International Journal of Human-Computer Studies” (Amsterdam: Elsevier) and co-author of “Wired for speech: How voice activates and advances the human-computer relationship” (Cambridge, MA: MIT Press).