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Mutual Fund Fact Sheet

Production Study

Noel Rodolfo is a Product Manager at Synthesis. He holds a Computer Science degree from Texas Tech University and an MBA from University of Illinois at Chicago.

In this study, Product Manager, Noel Rodolfo, explores how fund marketers compare when it comes to fact sheet release dates and overall production efficiency.

SynthesisTM

2014R E P O R T

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2014 STUDY: FUND FACT SHEET PRODUCTION

Here are some questions we wanted to answer:• What kind of throughput improvements can we get through document automation (versus manual production)?

• Arelargercompaniesmoreefficientthansmalleronesatproducingtheir fact sheets?

• How do our customers compare with their peers?

• Doesautomationtechnologyalwaysimproveproductionefficiency?

INTRO + PURPOSEFor the past 16 years, we’ve been in the business of helping marketing departments at investment managementfirmsgetahandleontheirproductionprocesses.Ourapproachcentersonapplyingcontentmanagement and data handling technology to create what we call document automation solutions. Note that I purposelydidn’tclaimthatwe“makefirmsefficient.”That’sbecause,inourexperience,technologymayindeedhelp--buttrueefficiencycomesfromaddingcontrolstoaprocess.Withpropercontrolsinplace,firmshavethetoolstomakeadjustmentstobecomemoreefficientand,asyouwillsee,stayefficient.

During many of our engagements, we’ve been privy to goals and metrics of investment marketers for fact sheet production schedules. To be honest, I’m not sure if these KPIs (key performance indicators) were initially driven by internal goal-setting or centered on an attempt to beat or match a competitor; most likely it’s a little of both. In any case, we know that fact sheet production schedules are driven by many factors including internal processes, sales and marketing initiatives, market demand, competition, etc.

Ifyouarereadingthis,youmusthavesomecuriosityabouthowyourownfirmcomparestoothers--right?

We too were curious and wanted to put together some actual metrics to share with our clients and prospects. Ourgoalistoprovidefundmarketerswithsomeempiricalinformationratherthanjustconjecture.

With these questions in mind, we started gathering data to see what we could uncover about mutual fund companyfactsheetreleasedatesandschedules.Withinthisdocument,wewillpresentaseriesoffindingsthatillustrate our interpretations of this data.

Wehopethatyouwillfindthisstudynotonlyinteresting,buttrulyusefulindevelopinggoalsormetricsthatcanbe used to improve your own processes. Please feel free to contact me directly at noelr@snth.com with any questions or feedback regarding this study.

I welcome your comments!

- Noel.

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2014 STUDY: FUND FACT SHEET PRODUCTION

THE STUDY METHODTHE SAMPLE SELECTION Starting from a list of 100 US-based mutual fund companies, we categorized each company into a small/medium/large bin based on number of funds supportedaswellasthefirms’assetsunder management (AUM). From this list, we chose approximately 12 from each bin at random, leaving us with36fundcompaniesforthisstudy.Someofthefirmswere specialists in one asset category or investment strategy while others spanned a wide spectrum of types.

We ultimately decided to remove one company from our sample. This was due to the fact that their fact sheet release dates fell outside of the 2nd standard deviation. (Which means they got a really, really late start!)

Sointheend,ourfinalstudyconsistedof35mutualfundcompaniesand175factsheets.

The following two histograms illustrate the sample distribution for number of funds (Figure 1) as well as Assets Under Management or AUM (Figure 2).

Figure 1 - Histogram of the study subjects by the number of funds supported by the firm/division.

Here’sthefinalbreakdown:

• 17firmsusingdocumentautomation

• 18firmsproducingdocumentsmanually

• 1firmdroppedfromthestudy(entireproductioncyclefelloutsideofthe2ndstd.deviation)

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2014 STUDY: FUND FACT SHEET PRODUCTION

InFigure1,wecanseethat19ofthe35firmssupport30fundsorfewerandtheremaining16firmsinthe sample quickly decline to single examples. This means that due to distribution bias, our study is going to be morestatisticallyaccuratewhenitcomestocomparingfirmswithbetween30and60products.

Figure 2 - Histogram of the study subjects by the firms’ assets under management (AUM $BIL)

Likewise,forAUMshowninFigure2,wecanseethat21firmsmanage$100Billionorfewer,3firmsmanagebetween$100Billionto$200Billion,etc.

Althoughwemadeanefforttoincludeanevennumberofsmall,medium,andlargecompaniesinourstudy,Figures 1 and 2 show that a disproportionate number of companies were on the smaller side.

We considered this to be acceptable for two reasons:

1. Our sample accurately reflects the industry (lots of smaller and mid-size firms)

2. In our sample, the number of automated vs. manual firms was fairly even

THE DATA COLLECTION PROCESS

Oncewedeterminedoursample,wevisitedeachofthefundcompanies’websiteswellafterquarter-end productionshouldhavebeencompleted.Oneachwebsite,welocatedanddownloadedfivefactsheetstousein the study. These samples were taken from various asset classes, if possible, and at random.

Information was taken from each of the PDF documents and logged into a database for analysis. The publish date was the most obvious piece of data to record, but we also picked up additional metadata which told us what type of system produced the documents -- manual or automated.

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2014 STUDY: FUND FACT SHEET PRODUCTION

Figure 3 - Histogram showing the range of fact sheet release by business day for the 35 firms and 175 documents in the study.

Finally,inFigure3,weshowahistogramdepictingthefrequencyofreleasedatesforeachofthe175factsheetsinthestudy.Forexample,thebusiestbusinessdayisthe11th,when27factsheetswerereleased. In fact, from this chart, we can clearly see that by far the most fact sheets were released on the 11th and 12th business days. Another way to look at this is that 36% of all fact sheets were released on these two days.

LIMITATIONS OF THE STUDY & POSSIBLE ERRORS

Certainly, this type of data gathering can introduce some level of errors and omissions. Some possible areas where errors may have been introduced are:

• Sample size (too small or too focused)

• Inconsistency of meta data available

• Information not available or incorrectly sourced

• Assumption errors

Despite the possibility of error, we felt that the resulting quality of the data was useful in providing insight into factsheetproductionschedulesandoverallefficiency.

36% of all fact sheets were released on the 11th and 12th business day after quarter-end.

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2014 STUDY: FUND FACT SHEET PRODUCTION

STUDY RESULTS QUICK FACTS:

• Thefirmthatpublishedthefirstfactsheetdidso manually.

• Theaverageautomatedfirmhas80Fundsandthe averagemanualfirmhas30funds.

• The 11th and 12th business days account for 36% of the document release dates in the sample.

• The average document release day is 13 business days.(Samplestandarddeviationis5.6businessdays)

• In terms of production cycle duration, the overall industry average is 6.8 business days. The automationaverageis5.6businessdaysandthe manualaverageis7.9businessdays.

• Theindustryaverageis11businessdaysuntilthefirst document is released. The automation average is 12 business days and the manual average is 10 business days.

• Onaverage,automatedfirmsproduce14.3factsheets perday,whilemanualfirmsproduceanaverageof 3.8factsheetsperday.Thatmeansautomatedfirms produceapproximately4xmorefactsheetsperday thanmanualfirms.

On average, automated fund companies produce 14.3 fact sheets per day, approximately 4 times more than manual firms.

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2014 STUDY: FUND FACT SHEET PRODUCTION

EFFICIENCY CHARTING Beforewepresentourkeyfindings,weneedtointroduceourProductionEfficiencyDiagram(Figure4below)and explain how to read it. This chart is integral to understanding our interpretations. It’s also important to notethatforthepurposesofthisstudy,wedefineefficientproductionasanearlyreleasedatealongwithashort production cycle. Following this chart, we will present our three key takeaways from this data.

Figure 4 - The Production Efficiency Diagram with the sample dataset plotted.

Efficient Production = early release & short production cycle

How to read this chart:1) Eachbubblerepresentsacompanyinourstudy.

2) Thesizeofthebubbleisrepresentativeofthefirmsize(bynumberoffunds).

3) The color represents manual vs. automated. Blue bubbles are companies that use automation for producing fact sheets, and the green bubbles are companies that manually produce them.

4) Horizontalaxisrepresentstheproductionstartday(estimatedbytheearliestdatefromsample).

5) Verticalaxisrepresentsthedurationofproduction(estimatedfromthesampletaken).

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2014 STUDY: FUND FACT SHEET PRODUCTION

KEY FINDING #1: Firms that produce their fact sheets manually are fairly consistent with their production efficiency

Whilelookingforpatternsintheproductionefficiencydiagramandthinkingofwaystodescribeourfindings,we were reminded of the concept of target groupings in marksmanship. When practicing a marksmanship sport like archery, a bullseye is important for scoring, but the grouping of shots show the consistency of the sportsman and the equipment.

So how does this relate to our study? We noticed that for the most part,firmsthatproducefactsheetsmanuallyarefairlyconsistentwiththeirproductionefficiency.Asevidence,justlookatthe“tightness”ofthegroupingofgreenbubbles(SeeFigure5).Thereareafew“flyers” (a marksmanship term again) of green, but for the most part everything istightlycentered.Ontheotherhand,eventhoughthereisanoticeablemaingroupingofblue,thereareafewflyersthatreallygotaway.Thisisinsyncwithourobservationsoutsideofthestudy,whicharethatfirmswhoproducetheirdocumentsmanuallycanbehighlyefficientiftheyhavereallymasteredtheirprocess.

Figure 5 - Production Efficiency and data visualization

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2014 STUDY: FUND FACT SHEET PRODUCTION

KEY FINDING #2: Automation alone doesn’t guarantee efficiency

AquickglanceatFigure6showsthatacoupleoftheautomatedfirmshavefallenoutofthepack.Or,goingbacktothemarksmanshipanalogy,thesetwoarethe“flyers”thatgotaway.

Figure 6 - Two automated firms that are no longer efficient.

TobetterdescribeandanalyzetheEfficiencyDiagram,let’sseparatethegraphintoquadrants(Figure7)anddescribe them (Table 1).

Figure 7 - Production Efficiency Quadrant Interpretation

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2014 STUDY: FUND FACT SHEET PRODUCTIONYoucanseefromFigures6and7thatthereweretwoautomatedfirmsthatfellclearlyintoquadrants1and4respectively, thus leaving them with some room for improvement.

Table 1 - Quadrants of the Efficiency Diagram (** contains firms that were on the border of the quadrants.)

Itisimpossibletoknowexactlywhythesetwoautomatedfirmsendedupinthe“roomforimprovement” quadrants and not in the optimal quadrant #3 with the majority of their peers. However, I have two guesses based on experience:

1. The availability of quality data will bottleneck any production process. Larger firms tend to have more diverse funds with more diverse data, potentially leading to this bottleneck.

2. Some firms put a huge weight and high priority on certain funds/products (often called “focus funds”). Many other funds and products then get a lower priority, which might result in a longer production cycle.

Having a good feedback loop and making continuous improvements is still necessary, even with automation inplace.Withautomation,afirm’sgoalshouldbetocreateasystemofcontrolsthatenablethemostefficientproduction cycle possible.

Quadrant Traits

Number of Firms

Description and Recommendations

QUADRANT #1: Early Start, but Long Duration 3**

Longproductioncyclesaredifficultonthestaffandleavespeoplewonderingwhereand when documents will be released. Firms in quadrant 1 should develop plans and strategy to shorten the production process.

QUADRANT #2: Late Start and Long Duration 0

Thefirmsthatfindthemselvesin quadrant 2 have much room for improve-ment. This is, by far, the least desirable quadrant to be in.

QUADRANT #3: Early Start and Short Duration 28

Firmsthatfindthemselvesinquadrant3have a good handle on their production cycle. There might be room for improve-mentswithregardstocosts,staffeffortor accuracy, but their timing and duration are within range of their peers.

QUADRANT #4: Late Start and Short Duration 4**

Althoughnotideal,firmsthatfindthem-selvesinquadrantIVaren’tdealingwiththesamekindofpainthatfirmsinquad-rant 1 and 2. These firms should look for areas to improve the 1st release date of fact sheet documents.

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2014 STUDY: FUND FACT SHEET PRODUCTION

KEY FINDING #3Automation can keep production duration low

InFigure8,wedrewaboxtoshowthefirmsthatcompletedtheirproductioninthesmallestduration– approximately5businessdays.TheefficiencydiagraminFigure8makesitclearthatautomatedfirmsarekeeping their production cycles short. This is illustrated by all of the blue bubbles that fall within the area.

This data supports the concept that once the automation system is in place, the bottleneck is mostly around theavailabilityofqualitydata.Withautomation,datashouldflowintothedocumentsandsharedcontentshould be updated across the entire document library.

Figure 8 - Firms that fall within this bounded & shaded area completed their production cycle in the smallest duration.

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2014 STUDY: FUND FACT SHEET PRODUCTION

CONCLUSION + LOOKING AHEADWe hope you found this report interesting as well as useful in your fund marketing work. We look forward to continuing our research on this topic, as we see a number of ways we can expand this study in the future.

For example:

• Continuing to grow the sample size from the number of firms included, to the number of fact sheet documents sampled.

• Repeating this study at some to be determined interval, in order to monitor consistency and changes between production cycles and over time.

• Adding additional document types such commentaries and/or web profiles. How do these correlate?

• Figuring out what the threshold is for going from manual production to automated production? Is there a magic formula that we can share with everyone?

• Introducing additional information such as asset class type (fixed income, equity, international, alternative, specialty, etc.) into the picture. What do the statistics look like broken down with asset types included?

• Conducting an industry survey to create self-reporting on document production methods and timelines.

To keep up with our latest insights on this topic, you can visit our blog at www.snth.com/blog or follow us on Twitter (@synthesistech). We look forward to seeing you!

For more white papers and resources, visit our website.

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2014 STUDY: FUND FACT SHEET PRODUCTION

Synthesis isaprivately-heldfinancialservicestechnologyfirm,establishedin1998.Weprovidemarketing, sales, and client communication solutions to asset and wealth managers, hedge funds, and retirement plan providers. We have a deep understanding of the challenges faced by investmentmarketers,andweofferauniquecombinationofexperienceandtechnologyto overcomethem.Oursoftwareplatformisrenownedforitsabilitytoadapttoanextremelywiderange of complex and evolving communication needs. From content management and publishing to data management and data quality, our clients use our solutions to produce beautifully compliant fund fact sheets, pitch books, performance charts & graphs, website content, commentaries, e-newsletters, and many other types of content.

For more white papers and resources, visit our website.

About SynthesisAutomation that takes your fund marketing to a whole new level

Fund Data Management and Data Quality: A better way to collect, store, and access clean fund data for marketing and reporting purposes.

Investment Marketing Automation: Quickly and accurately produce your data-driven investment marketing communications: fund fact sheets, commentaries, enrollment kits, client reports, & more.

Financial Sales Enablement: Enableyoursalesforceanddriveyourbusinessforwardwithbranded, personalized, and compliant pitch books and sales emails.

What’s your Story?Investment marketing can be chaotic, but it doesn’t have to be. With the right tools, fund marketers can streamlinetheirefforts,overcometheirchallenges,andenjoymoresuccess.Since1998,Synthesishasbeenworkingwithinvestmentmanagementfirmstoprovideintelligentmarketingsolutionsthatdrivebusiness forward. Drop us a line anytime. We would love to share our experiences with you.