We have a small feature announcement that either matters very little or matters an awful lot to you, depending on what you do with blist. Cells in date columns (including date/time) now understand what ‘yesterday’, ‘today’ and ‘tomorrow’ mean. That’s it for now. We don’t know what ‘a week from Tuesday’ or ‘the Saturday after St. Patricks Day’ mean. But for the 95% of the time when you want to enter a start date, an assigned on date or a resolved date and chances are that happened yesterday, today or tomorrow this little change is a big help.

Here’s a time sequenced screen progression to show you how it works. Click into a date cell. Notice there’s a calendar icon, which opens a date picker if you want it.

We don’t want to pick from the calendar. We just want to type ‘today’ into the cell.

And then as soon as we tab out of the cell, the string ‘today’ will be replaced with today’s date.

That’s all there is to it. I find it helpful and hope you do too.

To the casual observer, blist looks a lot like a visually rich spreadsheet. That’s by design. We want blist to seem familiar despite that under the cover it’s more a web-based database than a web-based spreadsheet. After all, spreadsheets are great for crunching numbers. But databases are much better for storing, finding and analyzing data.

One of the features that makes blist a database is the ability to create, save and execute lenses. A lens is a custom view of your data. A lens can include:

• Optionally a filter - criteria used to select a subset of the rows from the underlying full blist. Here are a few examples:
  • Lead Stage = In Closing
  • Varietal = Merlot AND Vintage BETWEEN 2003 AND 2005
  • Priority = High OR (Age > 90 AND Priority = Medium)
  • Opportunity > 1000000 AND Probability of Close = High AND State = CA

If you create a filter, you’ll only see those rows. The rest of the data is still there, it’s just concealed from you while you work through the lens. If don’t create a filter, you’ll see all of the rows.

• Optionally a column list - a subset of the columns that you want to see when viewing your data through this lens. As an example we use blist for our bug tracking system. It has 20 columns including a plain text field to hold the subject and a rich text field where we enter a much lengthier narrative description. Sometimes I just want a quick peek into open bugs so in my lens I hide about 10 of the columns I’m not currently interested in, including the description.

• Optionally a sort order of rows visible through your lens. Perhaps you want to see your sales leads by stage and then opportunity size. Maybe you want to view your open bugs by priority then severity then age. If you don’t specify a sort order the rows are displayed in their natural physical order, which is the order in which they were entered.

The user adoption pattern for lenses is interesting to observe. People operate without lenses for quite a while then all of sudden create dozens of them. I think it’s because there’s a barrier to overcome understanding them, then the lightbulb goes on, and people realize that lenses are a really powerful way to slice through data.

While there are times when going through the effort to create a temporary, disposable lens might make sense, the real power of lenses is that they can be saved, reused and shared. To change my view to any lens for the current blist, I just need to drop down the lens selector:

Hopefully you’re now excited to try lenses for yourself. Let’s make one. We’ll create a lens against the following hypothetical sales pipeline:

To start creating a lens, load up one of your blists then click on the lens builder icon or feel free to borrow the one above. It’s called “Hypothetical Sales Pipeline” and I’ve made it publicly accessible.

Click on the lens builder icon. I’ve circled it in red to show you where it is.

The lens builder has 3 tabs. The first tab is for creating your filter criteria. The second tab is for selecting the columns you want to show and which you want to hide. The third tab is for specifying the sort order.

The first tab - Filter Criteria - is initially empty:

Let’s find all of the leads where the stage is “Active Negotiations” and the opportunity size is greater than or equal to $2,500. Drag and drop the column called “Sales Stage” from the list of columns onto the filter canvas. Leave the condition combo box in the middle set to “Equals” then drop down the values combo box. It’s the rightmost combo box. Notice it has all of the sales stages. Find “Active Negotiations” in the list and select it. Next drag and drop the column “Oppty Size” on to the grid. Notice that an [And] button appears to connect the two conditions. That’s what we want, so leave it alone. (Under different circumstances, we might want either stage = “Active Negotiations” OR opportunity size >= $2,500. In that case we’d just click on the [And] button to flip it to [Or].) We want to change the condition from “Equals” to “Greater than or equal to” and then type in $2,500 into the rightmost field. When you’re done, it should look like this:

We don’t need all of the columns in the output. Let’s just include the name of the company, probability of close, the sales rep, and the opportunity size. Click on the columns tab and move all but those 4 columns from the list of visible columns to the list of hidden columns. You can move them by any of three methods -  dragging and dropping, using the left and right arrows in the middle, or double clicking. When complete, it will look like this:

Finally, let’s sort this in descending opportunity size order. Click on the “Sort By” tab. Move the column “Oppty Size” from the unsorted columns list to the sorted columns list. By default columns are sorted in ascending order. We want them to be listed in descending order, so click on the word “ascending” to invert it to “descending” order. It will look like this when you’re done:

We’re almost done. We could click [OK] and see the results and then perhaps discard the lens. But let’s instead give our lens a name in case we want to run this again in the future. Click on the [Save as...] and call it “Active Negotiations” then click [OK]. The lens has been saved by name and the results are displayed. Here’s what the results looks like:

That’s all there is to creating a lens in blist. Remember that a lens is a real-time view of your underlying data. If you load the same lens two days in a row, you will get different results if the underlying data has changed.

If you would like a more visual tutorial, we have a 2-minute blist video that shows you how to create a lens.

Kevin wrote a detailed guide to tagging blists and rows in blist a few days ago. You can also use row tags to highlight rows in different colors - all you have to is tag a row with the name of a color. The basic colors are all supported: red, orange, yellow, green, blue, purple - even pink, and black (which looks gray because the colors are semi-transparent). If you were so inclined you could even color your blist rows like a rainbow:

Go ahead and start highlighting rows.

Today we released 13 new “How Do I?” videos and an updated Getting Started Guide to help you learn blist.  This content is available in the product help section.  Enjoy and please let us know if there are other videos you’d like to see!

Here are HTML links to the videos, if you’re reading this from a source that doesn’t have flash support.

• Create a blist
• Import an Excel File
• Find a Template
• Add a Contact
• Share a blist
• Add a Table to a blist
• Filter and Sort
• Create a Pick List
• Print
• Upload a File
• Add a Photo
• Tag a blist
• Delete a blist
• Getting Started Guide
  

Lately we have been a little quiet on new feature announcements.  But fear not!  The team has been hard at work delivering stability and performance improvements to blist.  Over the last 2 weeks we’ve improved the stability of manipulating data within the grid and page view.  We have also made noticeable improvements to the performance of working with data.  There were numerous fixes that were the result of customer requests; so enjoy and — as always — keep the feedback coming!

One of the subtle but powerful features of blist is tagging. The use of tags is entirely optional but people who use them love them. I want to spend some time in this post to tell you how to use and create tags in blist.

You can tag a blist or specific rows within a blist. You can also tag lenses. At the lens or blist level tags are used to help describe your data set. Later when you are looking for a blist that you created a while back, you might be able to find it more easily by searching by keyword. When you perform a search by keyword blist automatically searches blist and lens tags.  You can enter tags for a blist or lens on the Information tab in the task pane:

Notice my NFL schedule blist is tagged with “football.” As such, if anyone searches in blist for “football” they will find my blist even though the word football isn’t part of the title. It’s one of the tags, so the blist search feature will find it.

As much as I like tagging blists and lenses, row tags are more interesting. At blist we use blist for our internal bug tracking system. Here’s a partial screenshot of “blistzilla” as we call it:

Notice that two of the bugs have a little green tag icon. When I hover my cursor over the tag, a tooltip pops up to show me the contents of the tag. We use row tags to loosely group related bugs. So those two that are tagged are marked as “lensbuilder” bugs. When a programmer is assigned one bug with a tag, he’ll often look to see if there are more bugs with the same tag so he can resolve them as a batch.

One of the really neat futures in blist is the ability to quickly filter by tag. The Task Pane on the right edge of the blist has lots of handy features including the Row Tags pane. If I open that, I can see a list of all the tags in all of the rows in my blist. By default they are sorted by frequency, but you can resort them alphabetically if you like. Here’s the Row Tags tab in the Task Pane:

To temporarily see only the 6 bugs tagged with “lensbuilder” I can simply click on that tag in the tag filter. As soon as I click on that entry in the list, my view of instantly changes to only show the matching rows:

Of course you might want to return to an unfiltered view of your data. Simply go back to the Row Tags tab in the Task Pane and click on the “Clear tag filter” link.

And just like that you’ll see all of the rows again, unfiltered.

You might be wondering how to tag rows in the first place. Simply select (highlight) one or more rows you want to tag, then bring up the row menu. In the row menu there is a Tags field:

That’s all there is to tagging rows in blist. When entering tags you can enter single words, multiple unrelated words separated by spaces or multi-word expressions in quotes.

Give tagging and filtering by tags in blist a try and let us know how it’s working for you.

Measuring and optimizing user retention, churn, and average revenue per user (ARPU) is of vital importance to the success of a web application. Not infrequently the cost to acquire a user is above and beyond the value or revenue that you get from that user – and you can’t make that discrepancy up on volume. If you bring in a user and they come back a second, or a third, or a hundredth time (I’ve certainly been to Amazon and Facebook more than 100 times) – you can amortize the cost of acquiring that user down to just about nothing.

Your business has a steady-state user base that starts with new user acquisition, which is dampened by loss in your initial conversion funnel, and balanced against the churn-out of existing users

This is just a long-winded way of saying that stickiness really, really matters, and you need to iterate your product to improve it, for example:

This graph compares three sample user retention curves over a four-week period after the week of account creation. Lets say the starting point for your web application is the blue graph, where 40% of users who join in one week return during the second week, 20% during the third week, 10% in the fourth week and 5% in week five.

Then you make some changes to your application, your user retention curve now looks like the red line, 50% of people come back in the second week, but the attrition and churn in subsequent weeks is faster than you had before. Perhaps you bought traffic and those new people were outside your core demographic, or perhaps you started sending weekly emails to users that brought them back a second time, but that started to rub people the wrong way.  The total percentage of users returning over that four-week period is higher – the sum of the percentages for the period is 87 versus 77 for the blue curve. But there’s a concern that as time goes on, every user is going to get sick of your application and leave for good.

Then you decide to tone down the aggressive emailing, you focused your messaging down to a narrower but more passionate audience, and added some really valuable content that you kept refreshing every week. Now your user retention curve looks like the green line. Only 30% of your new users come back in the second week, but people fanatically keep coming back again and again. The sum of the returning user percentages for the green line is 82, less than the red curve, but more than the blue curve. But looking forward, you can clearly see that as additional weeks pass – your active user-base is going to keep building, and the sum of percentages for the green curve will quickly outstrip what you would have had if you were living on either of the two previous curves. For the mathematically minded among you, that sum of percentages can be thought of as the area under the curve, or the integral of the user retention curve - there, you just did calculus!

Since blist is a collaborative product, we get a significant number of new users from existing users sharing blists and inviting friends and colleagues. And a significant amount of sharing and inviting happens after users spend a few weeks exploring blist, familiarizing themselves, finding value in the single-user case. If our users don’t stick around for at least weeks, we miss out on additional new users.

It’s well understood that breaking down your conversion funnel into steps and measuring the drop-off rate at every step – and removing friction at every point – is critical to succeeding on the web, and it’s equally important to measure and iterate on your product for retention as well, minimizing churn, amortizing the cost of user acquisition, maximizing the lifetime value of a user – and finally your average revenue per user or ARPU.

The right way to measure changes in user retention over time is ‘cohort analysis’ – comparing the percentage of users who, say, created accounts in one day, or week, or month who log-in, or take some other action, like buying a product, in subsequent days, weeks or months with other users who created their accounts in earlier and later periods. A good time-scale to start with for most web applications is week over week.

The basic question to ask and answer is: “Is the percentage of new users from last week who came back this week higher than the percentage of new users from two weeks ago who came back last week?”

For further reading, Josh Kopelman and Andrew Chen and Dave McClure have previously contributed great resources on retention, cohort analysis and ARPU.

Just a quick update to let you all know that Adobe had released the production version of Flash Player 10. The improvements are primarily in 2D and 3D rendering, but other improvements include richer text formatting, especially right to left text. Adobe also fixed a performance bug in Flash Player 9 that was especially apparent on the Mac. In other words, Flash Player 10 is much faster than Flash Player 9 on the Mac.

Many of us at blist have been using beta versions of Flash 10 for a couple of months and we recommend you upgrade from Flash 9 to Flash 10 at your earliest convenience.

You can download Flash Player 10 on Adobe’s website.

A number of SEO/SEM firms and consultants have been early adopters and active users of blist.

SEO/SEM firms are using blist both on the client side, managing clients, sales leads, and projects, and on the campaign/keyword side. Besides collaboration and keyword lists, blist can also store more visual creative like banner ad treatments and ad variations right in the grid using the photo datatype. You can also use the photo datatype to do things like store screenshots of particular organic search results pages and placements as well as snapshots of paid ad placements at particular moments in time and different geo-locations.

Pure Visibility is an SEO/SEM firm located in Ann Arbor, Michigan. Co-founder Catherine Juon describes how and why they are using blist today:

“I found blist through Twitter.
We are using it because we literally couldn’t find a tool that would allow us to manage competitive intelligence the way we wanted to. I wanted more than you can do via Excel - I wanted to easily be able to pull up a list of companies that met criteria a and b AND c…  And getting that third or fourth item is basically impossible in Excel.
We also are used to doing many things collaboratively via tools like basecamp, salesforce, etc…. which we *could* do via google docs… but you still have the same issue of being able to sort the data, which was really the whole point.”

If you work in SEO/SEM field, please take some time to run blist through its paces: www.blist.com

I’ve debated chiming in on the economic climate. It’s obvious that folks who’ve devoted their entire academic and professional careers are unable to rationalize what’s going on. What more can I add to the discussion?

There are a few thoughts I can add and those have to do with how bad economic conditions impact startups and how startups might want to navigate these difficult times.

The first reality that people need to come to terms with is that bad economic circumstances impact different companies differently, based on the stage of where the company is. For example, startups that had planned to raise an A round in late 2008 or early 2009 will likely fail at capitalizing their businesses in this climate. Second funding rounds will be equally hard, especially for companies that don’t show strong traction. Companies that recently closed rounds, however, might be in good shape to ride out the downturn.

My advice to startups is to be smart about turning a bad economy into a positive for your company. Here’s what I recommend:

1. Focus on making your best existing customers even more ecstatic. In politics they call this your base constituency, but in business these are your core customers. Keep them happy. They are making tough decisions too.
2. Reduce expenses to extend your runway. The quickest way to reduce expenses is by reducing payroll. Re-evaluate every individual in every role. Are they vital for your existence over the next 6 to 12 months? Are these individuals the ones you’d start the next company with? If the answer is “no” decide whether it makes sense to shrink the team a little.
3. Take advantage of the opportunity to catch up to the competition. Startups already know how to be productive even when resources aren’t abundant. Use your efficiency as a competitive advantage.
4. Accelerate your plans to generate revenue.
5. Nurture and develop strategic, long term business relationships. During boom times it’s easy to be distracted by all that’s going on, often at the expense of business development relationships that amplify distribution and reach. In slow periods, potential partners have time and you have time, so make the best of it.
6. Opportunistically upgrade the caliber of the team. If you have an underperformer or two, there may now be much stronger candidates in the market now. The goal is to come out of tough times even stronger than before.
7. Focus the team on core, top priorities. Now’s not the time to work around the margins.
8. Defer your external capitalization plans. If you have not already received a term sheet, assume angel or venture funding will not happen in the next 12 months.
9. Defer thoughts about your exit strategy.

I do believe there can be a silver lining in bad economic times. I founded MessageRite, an email archiving service, in late 2002. We felt we were able to catch up to our much bigger competition quickly in part because they were all stopped in their tracks, dealing with shrinking revenues and rounds of layoffs. Additionally we failed at raising capital during that economic climate, which forced us to figure out how to succeed with very modest resources. That meant that when we were ultimately acquired, the founding employees of the company owned all the equity which resulted in a much bigger payday for everyone involved. It was also interesting to note that our acquirer was one of the business partners we started working with during the tough times.