Email: The Bane and Boon of Modern Communication

February 25, 2013

Recently, we conducted a survey on digital work habits, specifically around email and its ubiquitous (and overwhelming) role in business communication. The survey results were eye-opening to stay the least. We found that a constantly increasing volume of emails are forcing knowledge workers to allocate significant time and effort to managing their inboxes.

Moreover, we were interested in getting feedback from experts in the productivity arena to learn how our results lined up with email productivity data at-large. When one of the top productivity gurus expressed an interest in writing about our findings; we were more than happy to oblige.

Below is productivity and time management expert Tara Rodden Robinson’s commentary on the Varonis Digital Work Habits Survey findings.

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Email is both the bane and boon of modern communication. According to a recent report, 144 billion (yes, billion, with a “b”) email messages are sent each day and nearly 70% of that traffic is spam. In a corporate setting, email still provides many benefits: it’s quick, provides documentation and information trails, and it’s convenient. But the continuous inflow, volume, and “leanness” of communication (that is, it’s lack of tone and context) make email one of the most complicated areas of information management in the business environment.

The Varonis survey on digital work habits sought to explore daily volume, how people manage their email, and determine the frequency and severity of email “mistakes” (such as a reply-all goof or forwarding sensitive or offensive materials to the wrong recipient). One of the key contributions of the resulting report was the division of results by job category; this is one of the few studies that offers insight into how C-level executives handle their email. (However, their sample size of C-level respondents was quite small so caution should be used in generalizing from these data.)

Here, I provide a commentary on the report based on my expertise with productivity and time management as well as my experience and background in coaching knowledge workers, including executives. I call out some highlights in the findings, make comparisons with data from other sources, and draw some conclusions.

Highlights From the Report

When a new client comes to me for coaching on time management, one of the first questions I ask is: How many emails do you receive each day? According to the Varonis survey, 67% of respondents received 50 or more emails per day with a small percentage (~5%) receiving more than 300 emails daily. Extrapolated, this amounts to 250 – 500 messages weekly or 1,000 – 2,000 messages per month. This makes it easy to see how an inbox can become inundated quite quickly. If representative, then it’s no wonder when people (sheepishly) report the number of emails stored in their inboxes as in the thousands.

The Varonis survey didn’t ask respondents how many total emails were in their inboxes but only how many “unread” emails there were. The vast majority of employees and managers reported having very few (less than 10 or zero) unread emails (~59% and ~70%, respectively). A small number of respondents claimed to be automating their email management with rules leaving me to surmise that practically every email message received must be reviewed individually in order to mark it as “read.” If, indeed, only 30 minutes are spent on email each day, as was reported, and a respondent receives, say, 100 messages daily, that would require a lightning fast processing time of 18 seconds per message.

When asked how they were processing email, the survey classified respondents into three categories: “filers” who empty their inboxes daily (presumably into some system of folders and deleting the remainder), “hoarders” who never delete anything but file and/or tag some proportion of their messages, “hybrids” who do a combination of filing and hoarding, and those who have “given up” on managing their messages. One might imagine that “filing” would be the most time consuming style, however, 65% of the filers reported spending 30 minutes or less each day on this task. (And I don’t know what to make of the 2.3% of filers who claim to spend “no time” on their email–they must use magic or have minions to do the work for them.)

The data for C-level respondents presented quite a different picture from the other two categories. In stark contrast to employees and managers, half of the C-level respondents report spending 30 minutes or more daily on email management. (The majority “employee” and “manager” respondents (59% and 63%, respectively) claim to spend 30 minutes or less each day on email.) One third of C-level respondents reported spending more than an hour each day on email (compared to 18% and 11%, employees and managers, respectively). Sadly, email management style (filer, hoarder, etc.) by job category was not included in the report. The number of unread emails for C-level executives was quite different from the other two groups as well. All C-level respondents reported having some unread emails (as opposed to a large number of employees and managers who claimed to have none); most C-level respondents had 10 or fewer, roughly 25% had 100 or less, and (gulp) nearly 20% claimed over 20,000 unread messages (one wonders what their boards would think if they knew!).

Comparisons with Data From Other Sources

Similar to the Varonis survey, the Radicati Group reports [pdf] that the average corporate employee receives roughly 60 emails per day. Thus, according to the Radicati Group, a worker processes roughly 100 emails per day (sent and received, together), a distinction that was not explored in the Varonis study.

According to the McKinsey Global Institute (MGI; 2012) report entitled The social economy: Unlocking value and productivity through social technologies, knowledge workers spend an average 28 hours each week (or roughly 5.6 hours per day) “writing emails, searching for information, and collaborating internally.” This includes “28% of work time reading, writing, or responding to e-mail,” which would break down to 13 hours a week (their average work week was 46.5 hours) or approximately 2.6 hours per day. In contrast, only 16.7% of the respondents in the Varonis survey report spending more than one hour per day on email however, the survey asked only about time spent managing email and didn’t specifically examine the time invested in other sorts of email related work.

Conclusions and Recommendations

One key question that is unanswered by the Varonis survey is: “How much of your work (that is, tasks) comes to you as email?” The number of requests that become actionable tasks varies greatly across the corporate landscape. In addition, the ability to delegate also varies from high (at the C-level) to none at all (for many managers and perhaps the majority of employees). Thus, knowing how much work (outside of the actual reading, writing, and managing) email represents would have been extremely useful to know.

In any event, I strongly recommend that workers separate task management from email management. The email inbox makes a very poor task management tool: the constant inflow of new items pushes unfinished work out of sight and messages must be read repeatedly to ascertain what is requested or is actionable. If workers are committed to being reliable and following through on what is requested of them, then the best way to track those commitments is to maintain a task list.

A second, widely reported email headache that went unexplored was the “cc” issue. When speaking to corporate audiences, excessive use of copying others on messages is one of the most vociferous complaints and one of the biggest drivers of volume. Thus, the number of emails received may be decoupled from the amount of actionable task content but messages may still demand a substantial investment of a worker’s time and attention. Surprisingly, a move from email to using social media may be a useful solution.

Luis Suarez, the IBM poster-child for going email-less, has reduced his inflow of email to practically nil and moved the vast majority of his communication to open, social channels. His rationale is that if his communications are openly available, fewer people will need to contact him directly. This reasoning is at the heart of the recommendations of the MGI report as well. By reducing the amount of information “locked up” in people’s inboxes and folders, MGI estimates that email use could be reduced by 25% (although Suarez’s personal experiment suggests individual gains could be much greater). This is an idea with legs: there are indications that numerous corporations are contemplating variations on social media that may reduce the primacy of email.

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Tara Rodden Robinson, Ph.D., is a productivity and time management expert. As an executive productivity coach, she provides one-on-one and team coaching services as well as speaking and training for corporate audiences. You can follow her on Twitter @TaraRodden or learn more about her by visiting her website: TaraRobinson.com


Homayoun Hatami: How companies use Big Data to find sales growth Mckinsey

October 24, 2012

McKinsey partner Homayoun Hatami cites examples of how companies used Big Data to drive growth. To take advantage of these opportunities, companies have to put big data at the heart of their sales organization — from recruiting talent to processes. Based on the book Sales Growth. Learn more at the Sales Growth site. [http://salesgrowth.mckinsey.com/]


Will Big Data Kill All But the Biggest Retailers?

October 8, 2012

Increasingly, the largest retailers in markets across the country are employing sophisticated personalized marketing and thereby becoming the primary shopping destination for a growing number of consumers. Meanwhile, other retailers in those markets, once vigorous competitors for those loyalties, are being relegated to the role of convenience stores.

In this war for customers, the ammunition is data — and lots of it. It began with transaction data and shopper data, which remain central. Now, however, they are being augmented by demographic data, in-store video monitoring, mobile-based location data from inside and outside the store, real-time social media feeds, third-party data appends, weather, and more. Retail has entered the era of Big Data.

Virtually every retailer recognizes the advantages that come with better customer intelligence. A McKinsey study released in May 2011 stated that, by using Big Data to the fullest, retailers stood to increase their operating margins by up to 60% — this, in an industry where net profit margins are often less than 2%. The biggest retailers are investing accordingly. dunnhumby, the analytics consultancy partnered with Kroger in the US market, employs upwards of 120 data analysts focused on Kroger alone.

Not every retailer, however, has the resources to keep up with sophisticated use of data. As large retailers convert secondary, lower-value shoppers into loyal, high-value shoppers, the growth in revenue is coming at the expense of competing retailers — all too often, independent and mid-market retailers. This part of the retail sector, representing an estimated third of total supermarkets, has long provided rich diversity in communities across the United States. But it is fast becoming cannon fodder.

Within the industry, the term used for this new form of advantage is shopper marketing, loosely defined as using strategic insights into shopper behavior to influence individual customers on their paths to purchase — and it is an advantage being bankrolled by consumer goods manufacturers’ marketing funds. A recently released study [pdf] by the Grocery Manufacturers Association (GMA) estimates annual industry spending on shopper marketing at over $50 billion, and growing.

The growth in shopper marketing budgets comes as manufacturers are reducing the spending on traditional trade promotion that has historically powered independent retail marketing. Past retail battles were fought with mass promotions that caused widespread collateral damage, often at expense to the retailer’s own margins. Today’s data sophistication enables surgical strikes aimed at specific shoppers and specific product purchases. A customer-intelligent retailer can mine its data searching for shoppers who have purchasing “gaps of opportunity,” such as the regular shopper who is not purchasing paper products, and targeting such customers with specific promotions to encourage them to add those items to their baskets next time they’re in the store.

A 2012 study by Kantar Retail shows manufacturer spending on trade promotion, measured as a percentage of gross sales, at the lowest level since 1999. But even this does not tell the whole story; it is the changing mix of manufacturer marketing expenditures that shows what is occurring. Trade promotion accounted for 44% of total marketing expenditures by manufacturers in 2011, lower than any other year in the past decade. This decrease is driven by a corresponding increase in shopper marketing expenditures.

As shopper marketing budgets have exploded, the perception has taken hold within the industry that a disproportionately large share of that funding is directed to the very largest retailers. That’s not surprising when you consider what Matthew Boyle of CNN Money reported recently. He noted that the partnership of Kroger and and dunnhumby “is generating millions in revenue by selling Kroger’s shopper data to consumer goods giants … 60 clients in all, 40% of which are Fortune 500 firms.” It is widely understood that Kroger is realizing over $100 million annually in incremental revenue from these efforts.

The Kantar Retail report goes on to say:

Manufacturers anticipate that changes in the next three years will revolve around continued trade integration with Shopper Marketing to maximize value in the face of continued margin demands. Manufacturers, in particular, expect to allocate trade funds more strategically in the future, as they shift to a “pay for performance” approach and more closely measure program and retailer performance.

 

The same report calls out that the future success model will involve deeper and more extensive collaboration between the retailer and brand, with focus on clear objectives and performance accountability. What needs to be recognized is that this manufacturer business model skews heavily to the capabilities of the largest retailers. It’s simply much easier for the brands to execute by deploying entire teams of people against a Safeway or Target or Walmart. It is much harder to interact with hundreds or thousands of independent retailers. Manufacturers’ past model of reaching independent retailers via wholesalers, who aggregated smaller merchants for marketing purposes, worked well in an age of mass promotion but not in an age of shopper-specific marketing. Wholesalers do not have shopper data, and do not have sophisticated technologies or expertise in mining the data. Meanwhile, they have a challenging record of promotion compliance, and in many cases lack the requisite scale for deep collaboration with brands.

Personalized marketing is proving to be a powerful tool, driving increased basket size, increased shopping visits, and increased retention over time. And if you’re one of the largest retailers, you get all these benefits paid for by CPG shopper marketing funds. But for everyone but those very large retailers, the present state of affairs is unsatisfactory. Independent retailers are keenly aware of the competitive threat and desperately want to engage, but they have not had the tools or scale to do so. The brand manufacturers are frustrated by increasing dependence on the very largest retailers even as they cave in to their inability to effectively and efficiently collaborate with a significant portion of the retail industry.

It would seem that the brand manufacturers’ traditional business model for marketing interaction with the independent retail sector is ripe for disruption. Growing consumer expectation for relevant marketing, the potential for gain if customer intelligence could be brought to the independent sector, and desire to mitigate the growing power of the largest retailers all provide powerful incentive to brand manufacturers. Independent retailers are savvy operators and are eager to join the fray if given the opportunity. Conversely, maintaining the status quo means the largest retailers continue to leverage personalized marketing to outpace smaller retailers, threatening the very diversity of the retail industry.

http://blogs.hbr.org/cs/2012/09/will_big_data_kill_all_but_the.html

 


The Jury Is In – CEO Choose Big Data Over Social Media

June 1, 2012

A new studyby McKinsey & Company reveals that less risky and potentially more beneficial realm of Big Data software is a higher priority today than social media integration. The study consisted of 1,500 surveyed CEOs, CFOs and CIOs between April 3 and April 12, 2012.

Almost 50% of respondents stated that they are currently using Big Data to “understand their customers better”, whereas 32% stated they are using social media for “interaction and promotion purposes.” The survey also found:

– 13% did not consider Big Data a priority, so far as stating it was “not on the agenda”

– Over 50% state that flexible delivery platforms are a priority for the next 1-2 business years

– 19% of respondents have deployed digital marketing practices across the enterprise

– 4% used location-based software to target customer promotions

The study also found:

– 52% believe that organizational structures not designed to take advantage of either Big Data or social media priorities

– 51% say that lack of technology infrastructure and IT systems are a significant challenge

– 43% and 31% are having difficulty in finding functional and IT talent, respectively

Big Data and social media do not have to be mutually exclusive. A number of businesses are beginning to integrate the two, using Big Data solutions to analyze business content based on their social media activity.

Thanks to http://blog.drjerryasmith.com/2012/05/31/the-jury-is-in-ceo-choose-big-data-over-social-media/


Big Data in the cloud…the ability to make business decisions

September 27, 2011

It’s no secret that while our devices may be shrinking, the amount of information we put on them is growing exponentially. In a study published in June 2011, IDC predicted that by the end of this year, the amount of information created and replicated will surpass 1.8 zettabytes, or 1.8 trillion gigabytes (this amount of data would fit onto about 28,125,000,000 iPods, each with 64 gigabytes of storage), and will grow by a factor of 9 in five years.

What is becoming more apparent is that big data is turning into big money, for enterprises and small businesses alike. So how can channel partners and cloud services providers (CSPs) help customers to manage navigate this trend?

With the development of big data trends and forecasts, a number of companies and groups have been formed to help manage, analyze and leverage it. According to an article by Stefan Groschupf, mining big data for business intelligence has led to the development of innovations like FlightCaster, a company that is able to predict flight delays by factoring in real-time conditions and using historical information on domestic flights.

In addition, many experts believe analyzing large datasets can be the precursor to more practical and cost-effective ways of doing business. A report by the McKinsey Global Institute, for example, estimates that retail firms that ‘maximize their use of big data’ could ‘increase operating margins by more than 60 percent…’.

The report mentions how Wal-Mart used electronic data to give suppliers a view of demand in its stores. With the collection of even more information on each store and buying habits of customers, retail chains like Wal-Mart can use the information they gather to tailor their business to the customer and improve operations.

And in a recent blog post on how companies are making money from the growth of data, Loraine Lawson noted that large tech security companies are crediting big data in part for boosting their company’s revenues.

Perspectives for Channel Partners

Executive Vice President at Asigra, Eran Farajun, says that cloud service providers are partly responsible for the explosion of data. With cloud computing and cloud services becoming more popular, the general public and small businesses are creating more and more data online, thanks to Facebook, LinkedIn, Twitter and others, being solely based online requires all the information created on those sites to be stored online.

So while data is being made, monitored and mined, who’s worrying about what happens if and when that information is lost?

Although most small businesses might not be thinking about big data problems now, Asigra’s Senior Director of Strategic Alliances, Ashar Baig says that in five to 10 years, big data is going to be a problem. In addition, having more, highly valuable information increases the vulnerability of what happens when that data is destroyed in an accident, irrecoverable or hacked. In this sense, being able to protect, backup and recover information becomes akin to insuring your house and car.

The debate over whether or not the public cloud is the answer to big data storage problems continues to unfold. For end-users, security in the cloud is already a touchy subject, much less backup and recovery. But with big data issues coming to the forefront,security becomes even more divisive.

Standardizing encryption in the cloud backup and recovery field is a start to ensuring the cloud can meet the demand that huge datasets will put on cloud storage providers, as more and more businesses rely on the cloud to store big data.

Considering the issues outlined above, cloud storage providers need to ensure their data encryption standards are consistent with industry leaders, such as the National Institute of Science and Technology in the United States, to be prepared for the oncoming challenge of big data.

Thanks to the guys at Asigra for the post.


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