The Big Data-Driven Business by Russel Glass and Sean Callahan give their perspective and examples around big data. This book was loaded with informatThe Big Data-Driven Business by Russel Glass and Sean Callahan give their perspective and examples around big data. This book was loaded with information. Here was what I will take away from this book:
The most effective strategy to get a 360 view of what the customers and prospects are doing is for companies to have a fully integrated marketing stack: MAP, CRM, Data management and Analytics tools. This will help serve up relevant messages at the right time and anticipate needs and create products the customer base did not even know they wanted. Companies benefit most when they bring all the data into one central repository. Other technologies to consider for big data in marketing are DSP, DMP, programmatic media buying, dynamic display ads and predictive leads scoring.
Over time we have seen the buyer’s journey has dramatically changed. In 2012, Forrester Research showed that a buyer could be 90% though the buyers journey before they contact a vendor. Buyers once relied on sales people for information, but now leverage the Internet to learn about products, read reviews and gain peer opinion through social networks. Companies need to find commercial insights that can help position them as a solution different from what the others in the market are offering and create content to educate the customer.
Through the marketing stack, marketing has access to the digital body language of customer & prospects. We can use retargeting or make specific offers based on this digital body language. Analyze your data to find what data points indicate that a prospect is ready to buy or a customer is ready to upgrade.
Driving leads that are based on data is central to the way the relationship between sales and marketing is constructed. Marketing should build extremely targeted and customized communication plans for sales. Marketing also needs should focus tactics to drive users through different stage of the funnel. At the top of the funnel, use display ads to drive brand awareness. In the mid-funnel, engage and educate users using display and social media ads that promoted tools such as ebooks, whitepaper & webinars. In the lower funnel, use retargeted display advertising to offer a free trial of services.
Break conversions into “soft conversions” such as blog post reads, watch videos or interacting with online content without supplying email and “hard conversions” where the user shares their email to get a white paper or other asset. Analyzing individual content pieces can provide insight into which ones are the most influential in driving prospects through the marketing funnel.
Leverage attribution models to determine the effectiveness of marketing tactics. Last click attribution tends give too much credit to the lower funnel without recognizing the efforts that got prospects into the funnel in the first place or nurturing and educating them to move them further into the funnel. It ignores the buyer's journey. Rules-based attribution tries to assign a values to a particular tactic based on predetermined rules or weights. Algorithmic attribution assigns values to each interaction based statistical regression to find the correlation between marketing activities and sales data. These data driven models typically provide the most accurate picture.
For big data, we should always be collecting and measuring. The enhanced measurement capabilities of digital marketing and the ease of A/B testing in digital environments enables marketers to put more money behind effective programs. Digital programs can be measured in real time and should be monitored on a weekly, daily AND hourly basis.
Stay on top of your data and processes around that data - technology is inextricably linked with a marketing's goals. Strategy and software are bound together. Conduct a data audit and strive to integrate data silos- a data audit can help identify what we have access to and give a better handle on what data we actually need to boost revenue and profits.
It is also important to leverage big data to challenge your business to deliver offerings that are cheaper and better. It is better to develop these new offerings yourself before others do. It is better to cannibalize yourself than be eaten by your competition. Think Blockbuster, Borders and Tower Records....more
The accidental creative: how to be brilliant at a moment has a very misleading title. This book says it will help you unlock you latent creative abiliThe accidental creative: how to be brilliant at a moment has a very misleading title. This book says it will help you unlock you latent creative abilities and create faster and more efficiently, but really what this book is about is how to organize your life and work. Once you really get into the book, you find that he is saying that to be creative, you must be organized, put in lots of prep time and set aside “idea time”. You must build meaningful relationships with others that may help you regularly with ideas and sharing. Your organization needs to include regular checkpoints to re-evaluate where you can adjust or cut projects and organize your work to avoid task switching and burnout.
The book was ok, I may try some of the ideas, but I felt like this was an overly structure life/work plan that will not work for many people. And it does not deliver on the promise of “how to be creative at a moment’s notice”. ...more
They do have some very different views in this book. I would not want to work in the crowded, overworked environment they describe, but I do like theThey do have some very different views in this book. I would not want to work in the crowded, overworked environment they describe, but I do like the idea of smaller meetings that only include people that are key to the meeting and basing decisions on data whenever possible. The LAX test was a good one, never hire anyone you would not want to get stuck with at the airport. ☺ I like the idea of ship and iterate, create the product ship it out, find the defects, improve and ship again. But I see this may not work for most companies that are not the in the software or web business. You can’t create a manufactured good using this method.
There are lots of great ideas in this book, it is worth the read to find them all. ...more
This book had great insights for people around start-up or tech companies.
He also talked about, as a tech company, how hard it is to stay ahead of theThis book had great insights for people around start-up or tech companies.
He also talked about, as a tech company, how hard it is to stay ahead of the competition. He emphasized that a tech company should strive to have a product that is at least 10 times better than your best competitor and he gave a wonderful example of how he saved his company through discovering and delivering what the top client would like from the product.
There were some interesting thoughts on when and why to sell you company -if you are number one and the market is growing, you should not sell at any price, think Google.
The book talks about how companies should take care of its people, products and profits in that order. First and foremost a company should be a good place to work to recruit and keep the top talent.
The book was mostly aimed at CEOs…such as how to find, hire, assimilate and fire executives. But he also discussed the importance of building an overall good corporate culture, the importance of communication and regular one on one meetings.
It was a good read for anyone working in a company that is going through a large amount of change. ...more
There are so many ways the numbers may be skewed. With the right data transformation, exclusions or imputations, the numbers can be manipulated to telThere are so many ways the numbers may be skewed. With the right data transformation, exclusions or imputations, the numbers can be manipulated to tell the story the researcher wants the data to tell. Always check raw data, the assumptions and methods used to transform or normalize the data and the statistical techniques selected to analyze the data.
The book gives many examples of data manipulated for some advantage, law school deans fudge the numbers to get higher law school rankings. Groupon shows the benefit of advertising with them, but Groupon looks at the number as a whole and does not break out current customers that take advantage of the discount from those that are net new customers.
The epilogue shows two data challenges I am very familiar with. To bad he does not have any quick fix for these: How do you get one system to accept the dates from another system as a date variable, not text or numeric? How do we categorize thousands of keywords into useful groups in a reasonable amount of time, especially considering these are always changing?
Fung reminds us that big data has nothing to say about causation, many things are correlated without one causing the other. He also demonstrates how statistical significance does not prove the results are important, tiny numbers with little real impact can be statistical significant.
Overall, I think the book was a good read. It had great examples for social data, marketing data, economics data and fantasy football. ...more
I really like this book. I very thought about things in this way, and it was good to see things from a different perspective. There were some great exI really like this book. I very thought about things in this way, and it was good to see things from a different perspective. There were some great examples. ...more
This book had some good advice for sales teams. I love his ideas around cold calling 2.0. But I was hoping this book at more information about how toThis book had some good advice for sales teams. I love his ideas around cold calling 2.0. But I was hoping this book at more information about how to use Salesforce.com. I really felt the author did not talk about the CRM system and how to use it, and how to govern data within the system....more