Home » Datamining
Category Archives: Datamining
At the third Startup Weekend event held in Lahore, Sahr Said‘s idea was crowned the winner. Within a week, she secured an incubation with the LUMS Center for Entrepreneurship and has embarked on a mission to make it incredibly easy for every woman to be beautiful.
We reached out to her to learn about what the company will look like in a year, from the perspectives of product, people, team, revenue and number of customers added.
How has your mission evolved?
Who doesn’t want to be beautiful? BeautyHooked.com’s mission is to provide ease of discovering and booking the ideal beauty service or beauty product with very personalized product recommendations. To that end, we aim to constantly evaluate our product market fit and refine our offering to best fit the market and its requirement.
Where do you see the venture a year from now?
Within a year, the company’s goal is to bring the very segmented beauty salon and spa industry online, making it more accessible and transparent to the end users. It will provide a simplified online booking process, so members can schedule their monthly maintenance, discover new beauty services and destinations or find a last minute appointment, 24/7.
What’s the big picture goal?
We envision to be the search tool for all things beauty; where customers can discover vetted beauty spots, read expert editorial reviews, and book appointments instantly with just the touch of a button. The product will allow women to seamlessly search, discover, review and book beauty services in close proximity, saving time and enabling them to make more informed decisions about their beauty needs and choices. At the same time, it will serve as a tool for the beauty services industry to connect with customers, generate traffic, get market feedback and promote their products and services to those women who need and want them the most.
How much are you charging users?
We’ll launch the MVP in mid-July 2015 and it will be free to use for all users so that they will be able to maximize this service at no cost to them. We are in the business of Beauty made easy, and within a year, our goal is to connect our customers with the beauty services they need, anytime, anyplace, anywhere.
ARTICLE BY DR. KOEN POWELS
Marketing measurement, accountability, analytics and dashboards are priorities in the toolkit of the successful Chief Marketing Officer. The pressure from the top is strong: prove your marketing is working, give us higher profits with lower budgets, show us the opportunities for profitable growth. Many companies have developed marketing analytic dashboards to help attain these goals, and are getting on average 8% more Return on Assets as a result (21% in highly competitive industries). A marketing analytic dashboard is a concise set of interconnected performance drivers to be viewed in common throughout the organization; for examples and case studies please see www.notsizedata.com.
A key challenge is how to motivate employees to actually use these analytic insights to improve decision making. Resistance to measurement and data-driven insights is widespread – not just among creative content generators who fear it will hamper their freedom (see http://analyticdashboards.wordpress.com/2014/03/13/help-your-creative-cats-bring-home-the-bacon-whos-afraid-of-accountability/’). Having lived through analytics & dashboards projects across 3 continents, I’d like to share tips on how to get employees on board.
My top ten list:
- Communicate the purpose and usefulness of a dashboard for the organization;
- Explain the role of an employee in the project and his/her impact on overall performance;
- Emphasize benefits of the dashboard application for an employee, e.g. ability to track, adjust, manage and consequently improve personal performance;
- Encourage a dashboard trial;
- Invite employee feedback and demonstrate that it is valued by adjusting the dashboard if possible;
- Inject a culture of accountability and facilitate a conscious choice of an employee to use a dashboard and other performance measurement tools, e.g. develop performance related incentive scheme that will motivate an employee to keep track of his/her individual progress;
- Incorporate a dashboard into day-to-day operations, e.g. use it in employee meetings;
- Garnering management support and guidance: Communicate the benefits of accountability culture for a company, e.g. give specific examples (supported with numbers) on how the company can optimize its expenditures and escalate its profits;
- Demonstrate functionality and usefulness of a dashboard application (for this purpose you may need to build a simple dashboard or select a dashboard example available online : see e.g. www.dashboardinaction.com;
- Indicate industry/market trends towards the use of a dashboard (show the statistics on dashboard adoption rate in the market, use industry or cross-industry benchmarks).
As detailed step for step in www.notsizedata.com, building a marketing analytic dashboard requires vision, courage, transparency and effective internal communication – which are much more important than the specific software or ‘big data’ used. Just like any other innovation, a marketing analytic dashboard cannot be effective unless its users understand its functions, are convinced about its benefits, and want to use it. In sum, a marketing analytic dashboard should not be something imposed on a company; it should be ‘sold’ in the best tradition of marketing art.
How about you? Can you share tips on how to motivate employees and increase marketing accountability? Looking forward to hearing from you,
Prof Koen Pauwels
“It’s not the Size of the Data – It’s how You Use It: Smarker Marketing with Dashboards and Analytics”
There has been a lot of fuzz about Google’s decision to not give Search Engine Search word conversion to your website. I recently wrote an article about what does Google look for on your website and there was already discussion about not getting the data anymore.
At first the data stream was cut from Google Analytics and other analytics tools, but Google Webmaster Tools still gave the listings. Today I found out that also Google Webmaster Tool access to SEO data has been cut off. I can still see, how many times my website has been listed for people searching and how many times my site has been clicked. However I can no longer see what context and content these clicks were related to. Although the data has been cut off, the customer help text still promotes content about Keywords and explain what they mean. There is no info about cutting off the keyword information.
What does this mean?
As a CMO you can’t know anymore, what content does actually drive traffic to your website. You can monitor content to which people are landing and make your insights. You can see search volumes from Google Adwords planner and recognize most used keywords to be used in your content creation. You can analyse your Google Adwords conversion success and use best working phrases in your own content production. However, the game just changed much more difficult than it used to be.
Here’s an approach to solve this challenge by KISSMetrics http://blog.kissmetrics.com/unlock-keyword-not-provided/ I think their advice has already become old – due to complete data shut down, but I’d love to hear about your solutions to SEO analysis in this situation. I’d also love to hear how this change has influenced your own media development!
Author: Toni Keskinen, Marketing Architect & Customer Journey Designer
Join FutureCMO Movement LinkedIn Group here
As we already know, organizations have ability to collect, store and analyze massive amount of data from multiple sources. They have spent a lot of money managing this big data. Investments in technology and analytics are useless unless employees can use data, information and knowledge in their decisions-making. Many companies are facing a big challenge in data-driven decision-making. Good and rich data won’t quarantee good decisions on their own. I posted May 2012 an article about Insight IQ. It was based on April 2012 issue of the Harvard Business Review where Shvetank Shah, Andrew Horn and Jaime Capella argue that Good Data Won’t Guarantee Good Decisions. In this article I continue this theme a bit further.
Shah, Horn and Capella found that there are three main types of decision makers:
- Visceral decision makers (gut feel decisions)
- Unquestioning empiricists (rely on the number alone)
- Informed skeptics (gut feel with data)
If we consider the matter in data perspective I defined simple data collaboration or data insight maturity scale. It emphasizes level and maturity of data collaboration in decision-making.
Organizations on the right ‘high side’ are capitalizing the data in decision-making. They combine the gut feel with data and can drive decisions that make a real difference in productivity and profitability. They can drive real competitive edge from and behind the data. They drive all necessary information from markets for better decision-making. They know their customers and customer journey. Although the decisions can be very complex with limited amount of time and there are many options to choose from, they can still make the decisions and carry out them fast. These companies are the winners. They have hit the jackpot, but unfortunately I see these kinds of companies quite seldom. However, that is the goal to go for.
Another way of looking at this variety in corporate behavior is to divide companies in three categories according to state of data maturity. There are three types of companies: data collectors, data analyzer and data innovators.
1. Data collectors
These companies are collecting and learning which data is important for their business. Light segmentation is used and it is more building and focusing on different customer groups. Analytical skills are very low and decisions are made as gut feel or based on basic sales figures and income statement
2. Data analyzer
These companies have systematic way of collecting and storing data. They are invested in the technologies that are available to analyze the data. They can do basic business reporting but are not able to drive the real value behind the data. They are good at analyzing and reporting but suffer the lack of interpretation skills to drive deeper business insight.
3. Data innovators
These companies are more comfortable with big data sets and technology. The focus is in how benefit from actionable insights and strategic big data. They have effective ways to share business information across functional areas that better decisions can be made. They have many data-savvy employees and data-driven decisions are informality in C-Suite.
Steps from data collector to data innovator
It is all about people, process and technology. Here are six steps to improve data-driven decision-making.
1. Emphasis on right data
- collect all relevant information into one single platform
- from business understanding to data understanding
- automate the data collection process
2. From silos to holistic business view
- map all the relevant data, which is essential for decision-making
- data preparation, consolidation and visualization are necessary
- CMO & CIO partnership
3. Choose the right technology
- find technology that meets your business demands
- choose technology, which accept and read data from any source
4. Online reality
- today’s business and business environment needs real-time data
- online data as enabler of quick decisions and insights
- rapid resource allocation
- online business management and intelligent decisions
5. Create strategic marketing dashboard
- KPI’s by channel
- true values from returns and investments
- identify risks and opportunities
- measure and lead the customer journey
6. Create systematic analytic skills competence development program for employees
- find the data-savvy workers in your organization
- make them to train everyone else of their analytics skills and method
- leverage analytics know-how widely in your organization
- C-suites as exemplary for others
Big data is out there. Data volumes, velocity and varieties have multiplied, in fact exploded. The big question is how to find the best ways to make all of it and how analyzed data is changing decision-making ways and management. Are big decisions based on analyzed evidence or intuition?
Many organizations are thinking how to drive the real business value behind big data. They are drowning with data and don’t know what to do with it. To drive competitive edge from big data, organizations need new competence and new management style. These are very big issues in today’s business environment and get accentuated all the time. For example, Gartner’s top 10 strategic technology trends for 2013 addressed strategic big data and actionable analytics. These two trends were number six and seven. And believe me, these two trends are raising top of that list the near future.
Ten years ago I created Intelligent Contact Management (ICM) concept at MicroMedia. It was holistic approach for sales and target marketing. The main idea was to translate inside and outside, online or offline data into information, information into deep knowledge, knowledge into action and action into measurable results. Action means in this case multichannel sales and marketing approach. Right and relevant message to right targets at the right time. Way of handling and managing touch points in customer journey. The concept of ICM presents in picture below.
Is there any significant changes in ten years. Nothing much, except tsunami of unstructured information has swept over. After ten years we are facing with information that comes in varieties and volumes we could ever imagine. That is not a threat, it is the big opportunity to handle and manage more precise information for predictions and decision-making.
Big data is a management revolution.
There was a very interesting article about big data and management revolution in Harvard Business Review, October 2012 issue by McAfee and Brynjolfsson. They wrote that “data-driven decisions are better decisions – it’s as simple as that. Using big data enables managers to decide on the basis of evidence rather than intuition. For that reason it has the potential to revolutionize management”. I could not agree more, using and analyzing data for evidence-based decision-making is the quantum leap from hunch and “super intuition”. This leap composes a big managerial challenge.
Are data-driven companies’ better performers?
There is a lot of buzz and skepticism about the real value of being data-driven organization. McAfee and Brynjolfsson made a research to find real evidence that using big data will lead to better business performance. They interviewed executives at 330 public North American companies about management practices, both organizational and technological and their annual reports. The major finding was that companies using big data and data-driven decision-making are performing better financially and operationally. In fact they are 5% more productive and 6% more profitable than their competitors. But, and this is a big but, 32% of respondents set their companies below 3 at 5-point composite scale when asking is your company data-driven. So, there is a lot of work to do in this field.
Destroy the empire of HiPPOs
Is your organization making decision HiPPO-style? McAfee’s and Brynjolfsson’s definition for HiPPO: the highest-paid person’s opinion. These executives are making decisions based on their experience and old intuitive patterns. The big question is are these executives ready to cancel their decisions if analyzed data is telling the opposite than their intuition and hunch. The answer is yes and no, but the big shift is happening. In today’s business world, executives can rely on data and predictions. The biggest factor is that they can ask the right questions. When the data-driven decision-making movement advances further, HiPPOs will mute and extinct. New data experts will arise. Future CMO will be this kind of expert in executive group.
Manage the big change
McAfee and Brynjolfsson picked up five management challenges when moving towards data-driven company and evidence-based decision-making.
- create leadership teams, set clear goals, define success and focus on asking the right question
- do not forget the human insight
- think creatively
- communicate clearly with employees, stockholders and stakeholders
- employ data scientists
- invest in technology
- update IT competence
- create cross-functional cooperation
- locate the big information and decision-making in the same place
- improve problem-solving techniques
- ask the right questions
- move actively away from hunches and instinct
- do not pretend be more data-driven than you really are
- forget the HiPPO-style decision-making
The big data will become a key basis of innovation, productivity and competition. According to research by MGI and McKinsey’s Business Technology Office there are five ways in which using big data can create value. First, big data can create value by making information transparent and usable widely inside the organization. Second, organizations can collect more accurate and detailed performance information, analyze it and drive performance. Companies are using big data and analysis to make better management decisions. Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Fourth, actionable analytics improve decision-making. Fifth, big data can improve the development of the next generation of products and services.
In the end, one rule of thumb to remember: Big data will never replace Big thinking!
I read a very interesting article from HBR, April 2012 issue. Shvetank Shah, Andrew Horne and Jaime Capella wrote an article about how good data won’t guarantee good decisions and most companies have too few analytics-savvy worker. If you are not able read that excellent article from HBR, here is a couple of points from it.
We have already discussed in this group about the new era of decision-making and importance of customer insight. Ability to collect, store and analyze the big data has grown explosively and companies spend a lot of money analyzing customer data. BUT. And this is a big but although you have the best BI tools ever but if your organization cannot capitalize it the investments are useless. Like Shah, Horne and Capella stated in the article: ”For all the breathless promises about the return on investment in Big Data, however, companies face a challenge. Investments in analytics can be useless, even harmful, unless employees can incorporate that data into complex decision-making. At this very moment, there’s an odds-on chance that someone in your organization is making a poor decision on the basis of the information that was enormously expensive to collect”
Shah, Horne and Canella created Insight IQ, method that asses the ability to find and analyze relevant information. They evaluate 5000 companies from 22 countries. The founding’s were interesting. Three groups were found: ”unquestioning empiricists”, visceral decision makers” and ”informed skeptics”
Companies are seeking for ”informed skeptics”. They are data-savvy workers who are able to make good decisions. They have strong analytic skills, ability to balance judgment and analysis. However, the study found that only 38% of employees and 50 % of senior managers fall into this group.
Shah, Horne and Canella identified four problems that prevent organizations from realizing better ROI in Big Data:
- Analytic skills are concentrated in too few employees
- IT needs to spend more time on the “I” and less on the “T”
- Reliable information exist, but it’s hard to find
- Business executives don’t manage information as well as they manage talent, capital and brand
Well, how to develop more informed skeptics? It demands constant competence development to increase data literacy and join information into decision-making. And of course, organizations have to give the right tools for analyzing the data. Ongoing coaching is essential and formalizing the decision-making process based on data and information. Shah, Horne and Capella stated that “many of the best data-driven cultures have formalized the decision-making process, setting up standard rules so that employees can get and correctly use the most appropriate data. Companies should make performance metrics transparent and embed the in job goals. They should also make sure that compensation systems reward dialogue and dissent. Great decisions often need diverse contributions, challenges, and second-guessing”.
Tiffany and BCBSNC are the great example of companies who have shown growing awareness of the pay-offs from Big Data and data literacy.
Is your organization underinvested in understanding the information and maximize Big Data ROI?
Source: Harvard Business Review April 2012,
Article: Good Data Won’t Guarantee Good Decisions
Writers: Shvetank Shah who leads the information technology practice at Corporate Executive Board, Andrew Horne and Jaime Capella, who anre managing directors at Corporate Executive Board