Miksi markkinoija on huono kasvuhakkeroija?

Mari Luukkainen kysyi LinkedInissä “Voiko kokenut markkinoija oppia kasvuhakkerointia?”

Marin kokemus on seuraavanlainen:

”Olen rekrytoinut kasvuhakkerointi- tai vastaavaan rooliin muutaman vuoden aikana 12 kokenutta markkinoijaa ja 25 henkilöä joilla ei ole ollut markkinoinnista juurikaan kokemusta.”

“Näistä henkilöistä tehokkaan ja systemaattisen kasvuhakkerointiprosessin oppi 0/12 ja 25/25.”

“Kasvuhakkeroinnissa olennaista on saada asiat hoidettua. Ainakin kerran viikossa pitää siis vetää läpi Analyysi-Hypoteesi-Priorisointi-Testaus, koska mitä nopeammin oppimiskokemuksia saadaan kerrytettyä, sitä nopeammin päästään tavoitteiden kanssa johonkin suuntaan.”

“Jostain syystä kokeneet markkinoijat eivät pääse tämän systemaattisuuden kanssa eteenpäin. Yleensä jumiin jäädään tuohon testiin, eli viikkotehtävää ei saada konkretian tasolla vietyä loppuun.”

Mari pyysi ihmisiä kommentoimaan mahdollisia syitä.

Ketjuun tuli yhteensä 27 vastausta. Tiivistän teemat tässä postauksessa.

(Huomaa, että kyse on yleistyksistä ja stereotypioista. Älä vedä hernettä nenääsi, vaan koita oppia jotain tästä postauksesta.)

KOHTA 1: Väärin opitut tavat

Ylivoimaisesti suurin teema vastauksissa on ”väärin opittu”.

Maarit Kauppila: ”Mennyt mututuntumalla toteutettu markkinointi voi jäädä tapoihin.”

Pasi Sillanpää: ”Sellainen, jolla ei ole kokemusta, ei omaa selkärangassaan totuttuja toimintatapoja.”

Jarno Tukiainen: “Tämä oikeastaan sama ongelma monessa asiassa näin tuotepiällikön silmin. Kokeneet ammattilaiset on tosi vaikea nyrjäyttää nopeasykliseen tekemiseen, jossa pyritään datalla todistamaan eteneminen jopa “virheitä” tahallisesti tehden. Aina joku outo käsijarru hirttää päälle.”

Opitut kokemukset johtavat vaihtoehtojen rajaamiseen aikaisemmin kokeiltuihin. Kuten Mikko Piippo kirjoittaa: “Markkinoija uskoo tietävänsä, mikä toimii ja mikä ei. Hän yleistää muutaman sattumanvaraisen havainnon yleiseksi totuudeksi: “Tämä toimi kerran – se siis toimii aina.””

Vaihtoehtojen poissulkeminen ennen kokeilua on vaarallista. Se on ns. boksin sisällä ajattelua, kun startupeissa pitäisi pyrkiä ”out of the box” -ajatteluun.

Väärin opitut tavat liittyvät yhtäältä virheelliseen markkinointikäsitykseen ja toisaalta puutteelliseen markkinoinnin koulutukseen. Kuvasin tätä ketjussa näin: “Vääristynyt kuva markkinoinnista on, että markkinointi on sitä miltä joku mainos tai verkkosivu näyttää — ei miten käytettävä tai toimiva se on.”

Väärin opitut tavat voivat tulla myös työkokemuksesta korporaatiomaailmassa.  Korporaatiokokemus on haitallista, koska se johtaa hitauteen: ”Kokemus itsessään ei ehkä tapa tuota vaadittavaa ketteryyttä, mutta kokemus hitaista, isoista, perinteisistä organisaatioista voi sen tehdä.” (Antti Jokela).

Itse kuvaan tätä termillä suunnittelukompleksi: ei tehdä, vaan suunnitellaan. Suunnittelu on hauskaa, mutta tekeminen on helvetillisen tuskallista.

KOHTA 2: Numerokammo

Mikko Piippo kirjoittaa: ”Markkinoijat ovat numerokammoisia. Excel- ja data ovat lähellä harvan markkinoijan sydäntä. Vielä vähemmän eurot ja sen analysointi tuleeko euroja tarpeeksi paljon enemmän sisään kuin lähtee ulos. Jos näin ei ole, “saatiin joka tapauksessa brändihyöty…”.”

Datan sijaan luotetaan intuitioon ja jo opittuun:

“Onko kokeneiden intohimona olleet analyysit ja “puolueeton” datan käyttö ja sen pohjalta toimiminen? Vai ajatellaanko, että osataan ja tiedetään jo niksit?” (Maarit Kauppila)

Yksi syy numerokammoon saattaa olla ns. mittaamattomuuden traditio, johon Leeni Heikkinen viittaa: ”Perinteiset markkinointinäkemykset täytyy ensin avartua ja ne voi olla syvällä. Minua on häirinnyt perinteiseen markkinointiin laitetun panostuksen ja tavoitteiden mittaamattomuus… ja mittaamisen hankaluus.”

Mitattavuus siinä mittakaavassa kuin se on mahdollista nykypäivänä on markkinoinnille verrattain uutta. Tosin pitää muistaa, että mittaamista ja testejä on markkinoinnissa tehty aina. Siksi onkin paradoksaalista, että edelleen vuonna 2020, markkinoijat eivät osaa (tai halua) mitata.

KOHTA 3: Kokonaisuuden hahmottamisen ongelma

Pasi Sillanpää kuvaa markkinoijille tyypillistä siiloutumista: “Voin (…) kuvitella, että yksi asia on kokonaisuuden hahmottamisen ongelma. Markkinointi on perinteisesti ollut sitä, että yksi ymmärtää asiakasta, toinen osaa sanoittaa ja kolmas kuvittaa. Joskus on niitä, jotka osaavat kaiken tuon.”

Pasi jatkaa: ”Näiden lisäksi on paljon markkinoinnin trafiikkia hoitaneita, jotka taas eivät ole tehneet sisältöjä, eivätkä aina ehdi edes tutustua markkinoinnin syvimpään ideaan.”

Itse kuvasin ketjussa asiaa näin: “Kokonaisvaltaisen liiketoiminnan ymmärtämisen puute. Kasvu ei ole “north star”, vaan näyttökerrat ja tykkäykset (vanity metrics).”

Harva markkinoija haastaa tuotekehitystä tai HR:ää, vaan ottaa nämä annettuna, keskittyen omiin tuttuihin ja turvallisiin aktiviteetteihin.

KOHTA 4: Pelko

Pelko johtaa moneen ongelmaan, tärkeimpinä hitaus ja tekemättömyys. Asiat ei liiku, jos ei rikota asioita.

On kahden tyyppistä pelkoa: yhtäältä pelko siitä mitä muut sanovat ja toisaalta pelko epäonnistumisesta.

Ensimmäinen pelko tarkoittaa sitä, että “tehdään markkinointia, joka on poliittisesti korrektia ja kaikessa varotaan astumasta toisten varpaille. Jos jotain tehdään, niin tehdään se tekonäppärästi eikä tavalla, joka haastaa kaverin liiketoimintaa aidosti.”

Toisin sanoen keskitytään lillukanvarsiin. Tehdään kampanja, ei uutta liiketoimintamallia.

Toinen on pelko feilaamisesta.

Sanni Juoperi: ”Datan pelko ja virheiden/epätäydellisen/shippaamisen pelko on varmasti toinen. Mut uskoisin niiden liittyvän enemmän asenteeseen kun taustaan.”

Jarno Tukiainen: ”Jostain syystä halutaan aina tunkea täydellistä maailmalle. Niin, ja puhun yleisesti kasvusta ja tuoteliiketoiminnasta, en pelkästään kasvuhakkeroinnista. ”

Sari Huovinen: “Kokeneilla ei ole sitä kokemusta, että saa oikein luvan kanssa “epäonnistua” kun tekemisestä toinen puoli heivataan ja toinen puoli jätetään, ja sitä kuuluu jatkaa kerta toisensa jälkeen. Voi olla, että kuuppa menee jumiin.”

Pelko on tuntemattoman pelkoa. Sitä ohjaavat markkinoinnin käytännöt eli se, miten markkinointia tehdään, ts. mitkä ovat ne aidot rutiinit ja toimenpiteet ihmisten jokapäiväisessä työssä.

Kuten Sanni mainitsee: “Uskaltaisin väittää että yksi iso tuska on se että vaikka kokeilut ja kasvu ”koetaan” tärkeiksi, niin ei kuitenkaan uskalleta tai pystytä luopumaan vanhasta eli hiotaan leiskoja, mietitään kokeilun ympärille kamppiskokokaisuutta, blogia ja orgaanisia someja pitää myös päivittää kuten myös myyntimatskuja jne.”

Totutusta on hankala luopua. Etenkin kun henkilö on ns. spesialisti eikä generalisti. Tietyssä mielessä kasvuhakkeroijan tulee olla aina generalisti eli omata perustaidot monelta alalta, ilman että tekee mitään näistä pelkästään (ns. T-malli).

Kasvuhakkeroija ei ole ”jack of all trades” eikä hän ole täydellinen – itse asiassa jokaisessa yksittäisessä osa-alueessa spesialisti on kasvuhakkeroijaa parempi. Kasvuhakkeroija tarkastelee koko kuvaa ja varmistaa, että hommat rullaavat.

KOHTA 5: Kampanjakeskeisyys

Kampanjakeskeisyys on startupeille äärimmäisen haitallista. Kampanja kestää vain hetken, jonka jälkeen sen tulokset ovat ohi (ns. haineväefekti).

Toista on esim. hakukoneoptimointi, jossa vaikkapa Elämyslahjoille vuonna 2012 tehdyt ländärit tuottavat liikennettä yhä tänä päivänä.

Tämän takia pitää ajatella ”assetteja”, ei kampanjoita. Assettien rakentaminen on insinöörimäistä säätämistä, eli “siinä rakennetaan automaatiopolkuja, hyvin konffattuja AdWords-kampanjoita ja SEO-prosesseja, joissa on jatkuvuutta.”

Mentaliteetti on aivan eri kuin ”brändimarkkinoinnissa”, jossa ”me tehdään tänä vuonna kaksi suurta kampanjaa ja kolme pientä, niitä mainostetaan telkkarissa, radiossa ja digissä”.

Korporaatioille massamainonnalla tehty brändimarkkinointi voi toimia, mutta startupeilla ei ole siihen resursseja. Startupeissa kaikki toimenpiteet pitää kytkeä kasvuun ja minimaalisten resurssien tehokkuuden optimointiin.

KOHTA 6: Keltaiset markkinointihörhöt

Tiina Käyhkö mainitsee tärkeän pointin eli ihmisten luontaiset mielenkiinnon kohteet.

Hän kirjoittaa: “Minulle tuli heti mieleen (…) ihmisten erilaiset käyttäytymistyylit. Systemaattisessa tekemisessä ns. siniset (tarkat insinöörityypit) ovat parhaimmillaan ja keltaiset (helposti innostuvat, mutta kyllästyvät pitkäjänteisyyteen) eivät. Kokeneissa markkinointi-ihmisissä on paljon keltaisia “markkinointihörhöjä” ;-).”

Markkinoijia voi kiinnostaa ”kauniit värit”, ”mainosten laatiminen”, ja ”näppärien sloganien laadinta”. Ainakin ne kiinnostivat minua, kun aloitin markkinointia lukemaan. Visuaalisuus vs. analyyttisuus jakaa ihmisiä. Kaikkia ei kiinnosta kokonaisvaltaisuus. Kaikki eivät halua optimoida. Ja se on okei.

Mutta näiden ihmisten ei välttämättä kannata hakeutua startup-maailmaan. He voivat työllistyä mainostoimistoon tai korporaatioon ja olla tyytyväisiä siellä.

Teemaan liittyy myös ”seksikkyyden ongelma”. Tarkoitan sitä, että kun jokin asia on hot, sen pariin ajautuu ihmisiä joille se ei ole se heidän juttu. Kun opetin kauppakorkeakoulussa digimarkkinointia, huomasin tätä usein – paljon tulijoita kurssille, mutta todella harvalla oikeasti intohimoa digiin taikka sen vaatimia kokonaisvaltaisia taitoja.

Ehkä meidän pitää hyväksyä se, että kaikista ei ole kasvuhakkeroijiksi. Ja edelleen: se on okei.

KOHTA 7: Väärin rakennetut organisaatiot

Keskeinen teema Marin opeissa on, että pitää olla kasvua tukeva (a) organisaatiorakenne ja (b) organisaatiokulttuuri. Rakenteeseen kuuluu rekrytoida Head of Growth, joka rikkoo siiloja eli tekee hommia niin tuotekehityksen kuin HR:nkin kanssa. Seurauksena on kokeilun kulttuuri, jossa joka hommassa voi kehittyä. Myös HR-tyyppi voi tehdä ”kasvuhakkerointia”, ei se ole markkinoinnin asia.

Kun kohdat (a) ja (b) eivät olla kunnossa, lopputuloksena on ettei ”osata pyytää”: ”(…) jos ei tule markkinointitaustalta, on vaikeampi keksiä itselleen kaikkea tuota muuta sälää eikä sitä muutkaan yrityksessä osaa pyytää.” (Sanni Juoperi)

Onko uudelleenohjelmointi mahdollista?

Kommenteissa korostui väärin opittujen asioiden teema. Sen pohjalta usea kommentoija ehdottaa uudelleenoppimista (”uudelleenohjelmointia”).

Poisoppimisen tärkeyttä korostaa Sari Huovinen: ”Kokeneet joutuu opettelemaan kolme kertaa, ensin pois vanhasta ja sitten uusi tilalle sekä sen että se ei ole epäonnistumista, kun osa tekemisestä heivataan. Kun nuo kuuppajumit saadaan vältettyä, niin lopputulos on varmasti hyvä.”

Pasi Sillanpää on samoilla linjoilla: ”On helppo kasvattaa täysin uutta henkilöstöä uuteen kulttuuriin, mutta pitkään uralla olleet pitää ensin opettaa pois totutusta ja sitten istuttaa sisään uusi.”

Jarno Tukiainen viittaa tähän termillä aivopesu: ”Jos osaisin vastata, niin täysimittainen aivopesu olis se temppu. Mindshiftin tekeminen on se vaikea asia ja jatkuvan oppimisen mentaliteetti.”

Jarno myös korostaa pienten onnistumisen kautta saatuja positiivisia kokemuksia: ”Eka oivallus usein on se, kun saadaan tuloksia jollain järjenvastaisella – siis aiemmin opitun luoman olettamuksen purkamisella.”

Sarin mukaan pelon estäminen on tärkeää: ”Perehdytys pitäisi olla erilainen kuin uudelle tabula rasalle, voisi korostaa sitä, että tarvitse, tai edes kuulu, yrittää tehdä kertalaakista jotain täydellistä.”

Kuinka löytää hyvä kasvuhakkeroija?

Susanna Junnila antoi seuraavat vinkit: ”työhaastattelussa voi kysyä: Mitä uutta olet oppinut viimeisen vuoden aikana? (Listan pituus on jonkinlainen signaali.) Ootko tehnyt viime duunissa asioita, jotka ei oikeastaan kuuluneet sulle? Mitä tekisit, jos nyt pitäis lähteä rakentamaan firmalle X markkinointi 4 viikossa? (Liittyykö siihen testailu vai “tietääkö” jo mikä toimii).”

Sari Huovinen: ”Valinnoissa kannattaa ehkä kallistua kokeneiden kohdalla kilpailuhenkisten persoonien puolelle, uskon että sen henkiset saavat nopeammin juonen päästä kiinni, kun saavat kilpailla itsensä kanssa.”

Miten markkinointia pitäisi opettaa?

Yksi keskeinen syy väärin opituille tavoille on puutteellinen markkinoinnin koulutus.

Kauppakorkeakoulussa ei opeteta perustaitoja kuten copywriting, A/B-testaaminen,  käytettävyys, asiakasryhmien segmentointi empaattisiin persooniin, hakukoneoptimointi, tuotekehityksen johtaminen, asiakkuuden elinkaariarvoajattelu, jne. Tästä on pari poikkeusta, mutta sääntönä on, että markkinoinnin opiskelijoita opetetaan tekemään asioita vaikeasti sen sijaan, että ne tehtäisiin tehokkaasti.

Esimerkiksi tilastollisesta testaamisesta tehdään helvetin monimutkainen SPSS-harjoitelma, vaikka yksinkertaisen t-testin (joka riittää lähes jokaiseen kasvuhakkerointieksperimenttiin) voi tehdä yksinkertaisella Excel-funktiolla.

Tästä vaikeudesta opiskelijat eivät myöskään oman kokemukseni mukaan pidä, sillä käytännön taitoja opettavat kurssit täyttyvät aina nopeasti. Opiskelijat siis haluavat oppia taitoja, mutta kauppakorkeakoulu ei niitä opeta, koska ”se on ammattikorkean tehtävä, meillä opetetaan strategiaa”.

Strategian opettaminen kaksikymppisille nuorille, joilta puuttuu peruskokemus firmoista, on typerää ja johtaa juuri näihin ongelmiin, joita tässä kirjoituksessa käsitellään.

Pääpointit:

  • Kasvuhakkerointi on erilaista markkinointia, jossa rikotaan siiloja.
  • Kasvuhakkeroinnin käytännön taitojen ja markkinoinnin opetuksen välillä on vakava kohtaanto-ongelma. Opiskelijat opetetaan ajattelemaan markkinoinnista väärin.
  • Samoin korporaatiokokemus opettaa vääriä taitoja ja ajattelumalleja.
  • Kokemuksen puute on siis hyve kasvumarkkinointia rekrytoitaessa, koska kaiken joutuu kuitenkin opettamaan tyhjästä.
  • Perinteinen markkinoija voi oppia kasvuhakkerointia, mutta jo opitun joutuu ensin oppimaan pois.
  • Kasvuhakkeroija on tietyssä mielessä manageri, mutta manageri, joka osaa myös tehdä. Ennen kaikkea hän varmistaa, että tehdään.
  • Kasvuhakkerointi ei ole yksittäinen taito vaan kokoelma erilaisia taitoja.

Viimeinen sitaatti tulee Marilta:

“Tärkeitä taitoja kasvuhakkerille on ymmärrys itseä ja muita kohtaan, kommunikointitaidot, projektinhallinta, järjestelmällisyys ja analyyttisyys. Substanssiosaaminen vaikkapa markkinoinnista on täysin epärelevanttia.”

Joni Salminen

Kirjoittaja on opettanut digitaalista markkinointia ja startup-markkinointia Turun kauppakorkeakoulussa useamman vuoden ajan. Näissä kursseissa kasvuhakkerointi on ollut yksi keskeinen ajattelumalli. Hän on myös ollut mukana moninkertaistamassa erään suomalaisen lahjaverkkokaupan liikevaihtoa soveltaen kasvuhakkeroinnin prinsiippejä käytännössä.

5 Trends of Coronavirus and the Economy

Outlining five trends I’m observing at the moment. NB: These are my personal opinions, mostly based on business news coming out and social media sentiments of people I’m following.

I: Monetary policy of lowering interest rates and “printing money” by buying bonds and stocks by central banks has a limited effect on alleviating the financial crisis. This is because, on one hand, frozen private spending cannot be replaced with these efforts. On the other hand, private investors are in state of fear and uncertainty that the monetary influx cannot mitigate, especially giving the historically long loose monetary policy that has eroded the psychological effect of printing money being the solution.

II: For the same reason, finance policy cannot prevent bankruptcies: it cannot replace private consumption. GDPs of Western societies typically consist more than two-thirds of service sectors — and this is mostly physical, not digital services (especially when looking at number of jobs provided). The lack of this type of economic activity results in immediate drop in demand for labor and thus unemployment. Bankruptcies are expected to follow once financial buffers of the small and medium sized companies are depleted. Finance policy can help by providing unemployment benefits after people have lost their job. It appears increased socialism is a must for surviving the crisis without society going into chaos of crime and looting.

III: What work is essential? Many people now observe that their work effort is really not “needed” — they stay at home and society can still provide functions for basic needs. Food, transportation, utilities… as long as these are provided, anything else is extra. Most people’s jobs are in the “extra” category and thus not necessary for the society to function in the short term. However, their spending on digital services, home delivery services etc. helps alleviate the crisis. This highlights the situation where a minority of workers “do the work” and the role of others is to “consume”, getting their money for consumption from surplus of economic activity.

IV: Inequality among workers that can “go remote” and those that cannot. The large physical service sector consists of jobs that cannot be done remotely (hotels, restaurants, airlines, drivers, leisure services such as cinema). Even those that can are being cancelled which is felt especially heavily by freelancers in creative industries (events, music/audio, etc.). The inequality is drastic: some continue working at home, posting Instagram picture of their “cool” home offices while others are scraping for survival.

V: Trade-off between health and economy. In the short term, society sacrifices economic growth for health, especially that of vulnerable groups, corresponding to a sort of tyranny by minority situation. The longer the crisis continues, the more likely this is to change: one’s people’s livelihood become at risk large-scale, they start less weighing the wellbeing of “others” and demand more wellbeing for themselves. This is especially likely to take place among the work-aged population that most suffers from the seizure of economic activity. Increased questioning of isolutionary measures is likely to take place by latest when the adverse effects hit the white-collar remote workers (marketers, HR consultants, software engineers, etc.). The world cannot stay on hold forever.

Your work is non-essential, according to coronavirus

Some of my observations about coronavirus and economy.

It’s striking how FEW people we need to sustain many. More than a billion people are in physical isolation, BUT utilities (electricity, water, internet) working perfectly. Really a marvel of innovation and automation that shows how well technology and infrastructure in most places has been built.

On the flipside, many people’s work is turning out to be NON-ESSENTIAL. That means, it is not *needed* for satisfying basic needs (food, shelter, and some form of psychological stimulus which is the internet). The interesting dynamics here is to know how long would the working class support the remote workers whose work is really not contributing to anything tangible. …on, the other hand, it does contribute since the remote workers are now the consumers and without consumers, we would not have producers.

There are different levels though, regarding choice (needs vs. wants). Utilities are not a question of choice (want), but that of NEED. I must have water. Somebody needs to do some work to get me that water. The guy doing that work is now thinking “why the hell i need to work for that guy when he’s getting paid (salary or government subsidy) for sitting at home. Let me stay at home as well.” This is the classical communist’s dilemma — people don’t have an incentive to make an effort if they can get the same pay-off without any effort.

The dilemma above is why this is now tricky. Government is needed to ensure an influx of money into the system. Otherwise, MOST people go out of work, because their work output is NON-ESSENTIAL. (Assuming here that the money influx is being used to keep people “working”, meaning they keep making nice and insightful LinkedIn posts like me here, while the real workers are running things.)

…one could also ask, with a good justification, how is this different from what was before? Perhaps in no way at all; coronavirus could be just revealing the fact that most people, no matter how busy or important they portray themselves as, contribute very little to the economy in terms of satisfying any basic need.

One more point is that of HISTORY: interestingly, we can see this pattern emerging since the dawn of civilization. What else was the birth of “clergy” class than the indirect consequence of production surplus? Because not all people were no longer needed to provide food and shelter, they MADE themselves useful by inventing stuff. And the people that did all the work accepted that, for whatever reason. The economy, and society in consequence, has always relied on NON-ESSENTIAL work, it seems.

The inspiration for this post comes from an encounter with a food delivery guy last week. He came to bring me food (=satisfy my basic need) and HE was apologetic for not finding the right place. I told him “no worries at all”, but in my mind I was thinking “dude, you’re saving me from hunger and YOU are apologizing. Can’t you see what’s going on here?”

Keywords: capitalism, Marxism, surplus, division of labor, needs vs. wants

Unit of cognitive effort

We should come up with a unit for cognitive effort. Like in information science you have a “bit” (binary digit that stores information). That concept was made famous by Claude Shannon who is considered as “the father of information theory”.

Here, I’m arguing a metric like “bit” should be developed for measuring cognitive effort of work tasks and individuals. Let’s call this hypothetical metric a “surge”.

So, a simple task would maybe take “5 surges” whereas a medium complex task would take “50 surges” and highly complex task could take “500 surges”. The relationship between different tasks and scale of surges is an empirical question, but I’m thinking the relationship might not be linear but geometric (because cognitive effort required by more complex tasks grows exponentially).

And a person, every one of us, has a certain inventory of surges per day. Like, let’s say Individual A has 10,000 surges because he ranks high on cognitive capacity. Another one, Individual B has only 5,000 surges, relating from poorer cognitive capacity. It’s important to acknowledge that while people differ biologically in terms of their cognitive skills, Individual A doesn’t necessarily perform better than Individual B. In contrary, Individual B can learn to better manage his surges, allocating them in the most optimal way to achieve his or her personal goals.

So, being aware of your “surge inventory” matters, as you can just as easily deplete your surges doing less meaningful tasks (from point of view of your professional or personal goals) or using them in a focused manner to spend less cognitive effort while still achieving the results. Thus, using mental energy becomes a “strategic game”. I believe many successful people already apply this method of sparingly allocating their cognitive effort, without necessarily being aware of it or being able to exactly quantify the process.

Quantifying cognitive effort is important because

  1. user tasks vary by how much cognitive effort (how many surges) they take on average
  2. individuals vary by how much it takes from them to complete the same task
  3. individuals vary by how many surges they have in their “inventory” per day (variation in biological and learned cognitive capacity)

A, b, can c have profound implications both for organizations that want to get things done more efficiently and for individuals that want to develop their skills in more efficiently handling work tasks.

Quantifying cognitive effort as “surges” and then measuring different tasks and individuals would help planning how to allocate resources for both organizations and individuals.

For example, if an organization wants to achieve a Goal X that comprises Tasks 1…n, and we know how many surges each task takes on average and how many surges our people have per day, we can calculate how many days it takes. This could be useful for startups, new product development organizations, or virtually anything. In my experience, currently the work efforts are specified in haphazard ways with very little systematic methodology. This can be one root cause as to why many development projects end up failing.

For individuals, quantifying cognitive effort helps systematically develop productive work habits by letting them evaluate the work effort (in cognitive, not temporal) terms before taking on work tasks. Stress and unrealistic expectations can be managed more efficiently this way, as one knows “I have only 500 surges left but my Tasks 1…n would require 2000 surges… I better acknowledge my limitations and choose to work on Task 2 that requires an estimate of 500 surges, leaving the rest for other days.”

(Some of the best workers I’ve seen are good because they can realistically evaluate the effort needed and don’t take on more they can handle — so, they are already subconsciously doing this.)

Finally, moving from “hours worked” to “cognitive effort taken” makes sense for modern knowledge-based work.

For modern knowledge-based work, time is a poor metric, since tasks vary so strongly by the effort they require. A 2 two-hour intensive session working on a problem is equivalent to 4 hours of simple routine task from a cognitive point of view, maybe even more.

In addition, for personal development of individuals, systematic understanding of where one’s “surge efficiency” is the highest would help reach better efficiency outcomes that would benefit both the workers and the employing organizations.

Some limitations of this thinking:

What’s the relationship between surges and success given the unpredicable nature of creativity in some work tasks? For example, highly complex scientific problems might see a lot of surges being spent on them without any visible result. I wouldn’t still say it’s a lost effort, as payoffs of this kind take the “hockey stick” shape — for a very long time, nothing. Then, an “overnight explosion”. Given this unpredictable nature of innovation, relying too strictly on measures such as “surges” would be counterproductive and wrong.

Let me know if any thoughts!

How to Audit International Facebook Advertising? A 37-Item Checklist

This is a joint article written with Mr. Tommi Salenius who works as a digital marketing specialist at Parcero Marketing Partners.

Introduction

Facebook advertising is a powerful form of online marketing for many purposes ranging from direct response campaigns to brand visibility and awareness. However, the competition in the ad platform is increasing every year, as companies are increasing their investments due to the fact that Facebook advertising, relatively speaking, works very well.

Figure 1 shows how Facebook’s revenue, comprising almost exclusively from advertising, has grown during the last nine years. Last year, almost $40,000,000,000 (that’s forty billion dollars) were spent on Facebook ads.

Figure 1. Facebook worldwide ad revenue statistics from Statista.com.

Increasing budgets imply increasing competition which means that in order to maintain the same visibility, advertisers need to increase their bids. For this purpose, in order to make profit in Facebook, advertisers need to continuously optimize their accounts.

To illustrate the power of Facebook advertising for online sales, Figure 2 shows an example from profitable Facebook account targeting direct online sales.

Figure 2. Example from Facebook account targeting direct online sales.

In this example, every euro invested in Facebook ads has generated direct online sales worth of €10. This means that with budget of €100,000 you can make sales worth of €1,000,000 if your target group is large enough and there is demand for your product (assuming that the sale grow linearly, of course).

The case of international Facebook advertising

Facebook is also one of the best choices to advertise globally, given its user base of more than two billion monthly active users (source: Statista.com).

Using the Locations feature in Facebook Ads targeting, several geographic targeting criteria can be chosen:

  • worldwide (type “Worldwide”)
  • country group or geographic region (e.g., type “in Europe”)
  • free trade area (e.g., type in “GCC, the Gulf Cooperation Council”)
  • sub-regions within a country (e.g., type in “Washington”)
  • other features (e.g., type in “Emerging markets”).

Figure 3 illustrates the Facebook targeting interface.

Figure 3. Targeting interface in Facebook Ads.

At the time of writing (October, 2018), the global targeting options in Facebook include the following:

Country groups

  • Africa
  • Asia
  • Caribbean
  • Central America
  • Europe
  • North America
  • Oceania
  • South America

Free Trade Areas

  • AFTA (ASEAN Free Trade Area)
  • APEC (Asia-Pacific Economic Cooperation)
  • CISFTA (Commonwealth of Independent States Free Trade Area)
  • EEA (European Economic Area)
  • GCC (Gulf Cooperation Council)
  • MERCOSUR
  • NAFTA (North American Free Trade Agreement)

Other Areas

  • Android app countries (paid)
  • Android app countries (all)
  • Emerging markets
  • Euro area
  • iTunes app store countries

Despite the tremendous potential of global advertising in Facebook Ads, companies often do not exploit this potential to the fullest. Moreover, we have observed that large international accounts tend to be messy and not well optimized. Therefore, in the following, we provide a checklist that can be used to audit such international Facebook Ads accounts.

Checklist for auditing international Facebook advertising

Here is a checklist for auditing Facebook paid advertising for international companies. This checklist is a concrete tool that can be used to evaluate your Facebook ad account’s current performance and identifying development areas that can get you toward desired results. There will be four sections: A) Account setup, B) Ad campaigns, C) Organic content, and D) International aspect.

Section A: Account setup

1. Is Facebook Business Manager activated? Benefit: Gain more control over user rights and possibility to operate with partners.

2. Is Facebook pixel is installed and configured? Benefit: Makes it possible to track business-related goals, for example, sales, visitors, blog reading times etc.

3. Is additional software being used besides Facebook Ad Platform? Benefit: Specific tools (e.g. Smartly, AdEspresso, Qwaya) can enhance Facebook performance by providing special features. If they are not used, at least they should be explored.

4. Is international Facebook page feature acclaimed? Benefit: This feature enables unified follower count for country pages but separated content on the country basis.

5. Is ‘business locations’ option used? Benefit: This feature enables to input specific geographic business locations.

Section B: Ad campaigns

6. Are Facebook campaign goals aligned with business goals? Benefit: The campaign goals (e.g. reach, engagement, traffic, sales, leads) should be traced back to overall marketing strategy to ensure they match what is wanted.

7. What is Facebook strategy of the current campaigns? Benefit: In auditing, it is useful to mentally classify the types of campaigns used in the ad account. These can include:

  • technology oriented — e.g., using dynamic ads for advanced targeting
  • content oriented — e.g., using creative concepts to get noticed
  • systematic advertising — i.e., customers need to be reminded regularly
  • ad hoc campaigns — i.e. running ads sporadically without clear purpose

8. Is there something that works already? Benefit: Verifying what already works enables to focus efforts on proven areas (e.g., some campaigns generate sales with low cost, data shows that specific creatives are working, different demographics are responding to ads).

9. Are there budget delivery problems? Benefit: Deliver issues are a common concern in Facebook Ads. Potential reasons: low ad relevance scores, low budget or bids, or not enough conversions (minimum 100 per month), wrong optimization goal. Solutions: change your optimization goal, e.g. from purchases to link clicks, test new target groups and ads, increase budget and bids.

10. Does campaign structure follow best practices? Benefit: Clear division of campaigns provides better tracktability and optimization. There should be different campaigns for all goals: prospecting and retargeting, upselling and cross-selling, reach and sales etc.

11. What auction type is used? Benefit: Auction vs. fixed price: with auction you get better results if you beat competition.

12. What placements are used? Benefit: Performance varies across placements, therefore, they should be tested. Facebook ad platform offers these placements: Facebook, Instagram, Audience Network, and Facebook Messenger. Based on our experiments, Audience Network usually performs poorly, and Instagram is more expensive than Facebook. Moreover, Messenger ads might be thought of more annoying than other placements because they are invading the user’s private space (the inbox).

13. What ad content types have been tested? Benefit: A good account has tested various different ad types (incl. carousel, link ad, instagram story, video, image, canvas).

14. What retargeting types have been used? Benefit: A good account has applied multiple retargeting types (incl. website retargeting, email retargeting, content retargeting).

15. What levels of retargeting are utilized? Benefit: A good account is “deep retargeting”, meaning that retargeting is specified to particular section of the website (e.g., main page, category pages, products pages, blog articles, cart, upselling, cross-selling).

16. What lookalike audience types are used? Benefit: Lookalike audiences can work because they retrieve similar users by “cross-polinating” the targeted subset of users with Facebook’s known information about other users. These options should have been tested (website, email, page likes, purchased lookalikes).

17. Is A/B testing performed systematically? Benefit: A/B test are a sign of active campaign management (both ad set and ad level). Facebook Ads provides a native option for A/B testing as a special campaign type (this campaign type can be used e.g. for testing different creatives, target groups or technical settings).

18. How well are the assets structured? Benefit: Clear naming principles make it easier to analyze and optimize (e.g., are campaigns, ad sets, and ads named systematically).

19. Is UTM tagging used? Benefit: UTM parameters enable tracking visitor performance in other analytics software, such as Google Analytics. The tagging can be done manually or automatically; the main point is that it should be done.

20. What attribution model is used? Benefit: Choosing a different attribution model can drastically change the interpretation of account performance. There are two types of conversions in Facebook: view conversions and click conversions. To get a more conversative view, include only the click conversions with a short attribution window (e.g., 1 day). To get a more rosy picture, include view conversions with a long attribution window (e.g., 28 days). There is no absolutely right or wrong attribution model.

21. Is dynamic advertising used? Benefits:

  • dynamic advertising can be used both in retargeting and in new customer acquisition
  • it offers wide range of options, if technical setup is made correctly, e.g., automated price promotions

22. Is advanced configuration of dynamic advertising used? Benefit: This is underused, yet highly potential feature of Facebook Ads — it enables to customize automatic advertising (e.g., prefer products with high gross margin, geographically show right products for right areas).

23. Are rules used for optimization? Benefit: Rules enable the monitoring and automatic response to business critical conditions (e.g., notification from data anomalies, adjusting budget based on results etc.).

24. Is the budget spent effectively? Benefit: Facebook Ads can waste budget, but there can also be much potential for upscaling the spend — based on performance metrics, one should analyze if the budget should be decrease/increased, what is the potential reach of target groups, how well are those target groups reached, and with what impression frequency.

25. What bid strategy is used? Benefit: A good account has tested several options, including: Lowest cost (standard), lowest cost with bid cap (risk of delivery issues), or Target cost (can be used for scaling up the budget).

Section C: Organic content

26. Is there enough quality content to be believable on the eyes of customers if they visit the Facebook page? Benefit: Visitors may want to check the quality of the page. Having little or no organic content creates mistrust.

27. How active are the Facebook followers of the page? Benefit: There can be a possibility to get insights from followers or turn their enthusiasm into more business. Engagement rate is a good metric, i.e. divide post responses by post impressions.

28. Is organic content reaching the target group? Benefit: If not, maybe it should be advertised. Many Facebook pages produce fairly good content that reaches nobody organically.

29. Is there point of focusing organic content or paid advertising? Benefit: The strategic roles of organic and paid should be addressed. What is the role of organic content? What is the role of paid advertising? Note: multiple ads can be advertised and A/B tested without publishing these on the news feed.

Section D: International aspect

30. Are the ads translated? When doing advertising to e.g. 10 countries with different languages, the ads should also be communicated in 10 different languages. Note that one country can contain multiple language groups, requiring localization even within a single country.

31. Is campaign structure supporting multiple languages? Each language should have been placed in separate target groups. For example, campaign could be name after the country, and it should contain different ad groups for each languages.

32. Is there enough budget to advertise internationally to all target groups? If you are targeting several countries, cities, and languages, these all need different budgets. In order to make impact, it is not usually wise to divide budget into too small pieces.

33. Is there other localization besides translation? Often, an error is made to assume localization is only about language. However, it is also about culture, customs, and ethnicity. For example, value propositions of communicated benefits may be entirely different when the same product is promoted to culturally different target groups (e.g., collectivity-individuality aspect might differ). Another example is that imagery matters for ethnic match between the target audience and people shown in the ads.

34. Have the country-basis legal restrictions been taken into consideration? E.g. different countries have different restrictions for promoting alcohol products, and European countries have strict orders for handling the data according to GDPR protocol.

35. How do normalized metrics vary by countries? Compare performance by normalized metrics (e.g., ROI), because that adjusts for variation between the markets. For example, Facebook Ads bids can be ten times more expensive in the US than in Vietnam. Similarly, purchase power differs so avg. conversion value can be one tenth in Vietnam, meaning that advertising would be equally profitable. To account for this, use normalized metrics, such as ROI or ROAS.

36. What are the city-level performance differences? Another common mistake is to assume that country is detailed enough segmentation criteria for performance differences. However, performance can vary greatly by city, e.g. in big countries like China or US. Moreover, rural areas can differ compared to city areas because people’s tastes, values, and behavior is different. To accommodate for this, Facebook advertisers should segment by city in addition to country (e.g., compare TOP 5 cities of each country).

37. What are the segment similarities across countries? Each impression has a cost. And each impression also adds information about customer responses. However, in the Facebook Ads account the performance values are siloed across different campaigns and ad sets. Therefore, to optimize such accounts, data needs to be combined. For example, if targeting 12 countries, the performance by demographic groups can be aggregated to give more statistical power (higher reliability for found similarities and differences).

Conclusion

This list of 37 items is a good starting point for analysing any Facebook Ads account running international campaigns. Besides these steps, Facebook account level data can be used for analysis purposes to find patterns in the data. For example, making country level breakdowns is made easy in the user interface of Facebook Ads platform.

About the authors:

Tommi Salenius is a Digital Marketing Manager at Elämyslahjat.fi, a Finnish e-commerce company that sells experience gifts. Tommi also works at Parcero Marketing Partners as a Lead Digital Marketing Strategist. www.tommisalenius.com

Joni Salminen is a Digital Marketing Manager at Elämyslahjat.fi, a Finnish e-commerce company selling experience gifts. Joni is also a board member at Konvertigo Digital Agency that runs digital marketing campaigns to over 100 countries. www.jonisalminen.com

How to identify useful user feedback? Three tips for value driven-user development

In our APG team (APG = Automatic Persona Generation), we have the goal of doing value-driven system development. “Value-driven” means that each feature we add or incorporate, solves a real user problem (i.e., provides real value). Since our clients are typically operating in the business domain, their problems deal with understanding their customers better. That’s the space APG operates in.

To discover real user needs, we’ve been carrying out several user studies about personas. However, there are many issues in conducting user studies. The feedback we get is not always relevant or valid.

For example, some participants might not be truly engaged or interested in the system and just participate out of duty or because they were “forced to”. Similarly, users may just brainstorm features that really they would not use but that “sound cool”.

Moreover, when compiling the feedback, we find that there are a lot of requests for new features. Say, the users want 10 new features, but we have time and resources for two and therefore need to prioritize.

Below, I’m sharing three principles we’ve developed in order to cope with these situations.

1. Who does the feeback come from? => not all people are engaged, motivated, or knowlegeable to give useful feedback. Therefore, we have to consider if a person is just “shooting ideas” or if he or she actually wants to provide useful feedback. We then prioritize the comments from the people whose feedback indicates they are taking the commenting more seriously.

2. How repetitive is the feedback? => if the request comes from many organizations and many people within an organization, it is more likely to be a real problem to solve. If it’s a rare request, the problem is probably also very rare and worthy to focus on.

3. Is the feedback traceable to a real problem the user has? => this question tries to clarify if the request if a nice-to-have or pain killer. We need to solve real problems with the system, so nice-to-haves need to be minimized. Even if many motivated people suggest a new feature, it could still be a nice-to-have if we cannot logically connect it to a real problem.

Conclusion

Nice-to-have features are like a disease; everything can be done, but only a few things are worth doing. With nice-to-have-features, the system will not have active usage. The goal of value-driven development is to develop a system that has real users that actively use it.

Therefore, focusing on distinguishing the most useful feedback from a lot of interviews, think-alouds and comments is crucial, especially for small teams and startups that are forced to focus their development efforts.

Automatic Analytics: Considerations for Building User-Oriented Data Analytics Systems

This is an unpublished exploratory study we wrote with Professor Jim Jansen for Machine Learning and Data Analytics Symposium (MLDAS2018), held in Doha, Qatar.

Change of landscape: For a long time, automation has been invading the field of marketing. Examples of marketing automation include the various scripts, rules, and software solutions that optimize pay-per-click spending, machine learning techniques utilized in targeting of display advertising (Google, 2017), automated tools that generate ad copy variations, and Web analytics platforms that automatically monitor the health of marketing performance, alerting the end users automatically in case of anomalies.

In particular, several steps of progress towards automating analytics insights are currently being made in the industry and research fronts of data analytics.

For example, there are several tools providing automated reporting functions (e.g., Google Analytics, TenScores, Quill Engage, etc.). While some of these tools require pre-configuration such as creating report templates, it is becoming more common that the tool itself chooses the relevant insights it wants to portray, and then delivers these insights to the decision makers, typically pinging via email. An example of such an approach is provided in Figure 1 that shows Quill Engage, a tool that automatically creates fluent text reports from Google Analytics data.

Figure 1: Quill Engage. The Tool Automatically Generates Fluent Reports From Google Analytics Data, And Provides Numerical Comparisons Based on Outliers And Trends.

As can be seen from Figure 1, the automatic analytics tool quickly displays key information and then aims to provide context to explain the trends in the key performance indicators.

Benefits: The benefits of automatic analytics are obvious. First of all, automation spares decision makers’ time, as they are not forced to log into systems, but receive the insights conveniently to their email inboxes and can rapidly take action. Since cognitive limitations (Tversky & Kahneman, 1974) are imposing serious constraints for decision makers dealing with ever-increasing amounts of “big data,” the need for smart tools that pre-process and mine the data at the user’s convenience are highly beneficial.

The core issue that automatic analytics is solving is complexity.

As a marketing manager, one has many platforms to manage and many campaigns to run within each platform. Multiple data sources, platforms, and metrics quickly introduce a degree of complexity that hinders effective processing of information by human beings, constrained by limitations of cognitive capacity.

In general, there are two primary use cases for business analytics: (1) deep analyses that provide strategic insights, and (2) day-to-day analyses that provide operational or tactical support. While one periodically needs to perform deep analyses on strategic matters, such as updating online marketing strategy, creating a new website structure, etc., the daily decisions cannot afford a thorough use of tens of reports and hundreds of potential metrics. That is why many reports and metrics are not used by decision makers in the industry at all.

The solution to this condition has to be automation: the systems have to direct human users’ attention toward noteworthy things. This means detecting anomalies on marketing performance, predicting their impact and presenting them in actionable format to decision makers, preferably by pinging them via email or other channels, such as SMS. The systems could even directly create tasks and push them to project management applications like Trello. A requisite to automatic analytics should therefore be the well-known SMART formula, meaning that Specific, Measurable, Appropriate, Realistic and Timely goals (Shahin & Mahbod, 2007). Through this principle, decision makers are able to rapidly turn insights into action.

Interfaces for automatic analytics: To accomplish the goal of automatic analytics, one trending area of is natural language systems, where users find the information by asking the system questions in free format. For example, previously, Google Analytics had a feature called Intelligence Events, which detected anomalies. Currently, Google provides automatic insights via a mobile app, in which the user can ask the system in natural language to provide information. An example of this is provided in Figure 2.

Figure 2: Screenshot from Google Analytics Android App, Showing the Functionality of Asking Questions From the Analytics System.

However, even asking the system requires effort and prior knowledge. For example, what if the question is not relevant or misses an important trend in the data? For such cases, the system must anticipate, and in fact analyze the data beforehand. This form of “intelligent anticipation” is a central feature in automatic analytics systems.

Examples: In the following, we provide some examples of current state-of-the-art tools of automatic analytics. We then generalize some principles and guidelines based on an overview of these tools.

First, in Figure 3, we present a screenshot from email sent by TenScores, a tool that automatically scans Quality Scores for Google AdWords campaigns.

Figure 3: TenScores, the Automatic Quality Score Monitoring Tool.

In search-engine advertising, Quality Scores are important because they influence the click prices paid by the advertisers (Jansen & Schuster, 2011; Salminen, 2009). In this particular case, the tool informs when there is a change in the average Quality Score of the account.

From a user experience perspective, the threshold to alerting the user is set to very low change, resulting in many emails sent to the users. This highlights the risk of automation becoming “spammy,” leading into losing user interest. The correct threshold should be set experimentally, e.g., according to open rates by experimenting with different increments of messaging frequency and impact thresholds.

In Figure 4, we can see a popular Finnish online marketplace, Tori.fi. Tori sends automatic emails to its corporate clients, showing how their listings have performed compared to previous period, and enabling the corporate clients to take direct action from within the email.

From example, one can click the blue button and the particularly listing which is not performing well, is boosted. In addition, there is a separate section (not visible from the screenshot) showing the best performing listings.

Figure 4: Tori’s Marketplace Insights Automatically Delivered to Inbox.

Risks: There are also risks associated with automatic analytics. For example, In search-engine advertising, brands are bidding against one another (Jansen & Schuster, 2011). Thus, an obvious step to further optimize their revenue by providing transparent auction information is Google sending automatic emails when the relative position (i.e., competitiveness) of a brand decreases, prompting advertisers to take action.

This potential scenario also raises questions about morality and ethics of automated analytics, especially in click auctions where the platform owners have an incentive to recommend actions that inflate click prices (Salminen, 2009). For example, in another online advertising platform, Bing Ads, the “Opportunities” feature gives suggestions marketers can implement in a click of a button. However, many of these suggestions relate to increasing the bid prices (see Figure 5).

Figure 5: An example of Bing Ads Recommending to Increase Keyword Bid.

If the default recommendation is always to raise bids, the feature does not add value to end user but might in fact destroy it. From an end user point of view, therefore, managers are encouraged to take recommendations with a grain of salt in such cases. From a research point of view, it is an interesting question to find out how much the automatic recommendations drive user actions.

Discussion: The current situation is that marketing optimization consist of various micro-tasks that are inter-connected and require analytics skills and creativity to be solved in an optimal way. The role of automated analytics, at least with the current maturity of technology, is pre-filtering this space of potential tasks into a number that is manageable to human decision makers, and, potentially, assigning the tasks priority according to their predicted performance impact.

In this scenario, humans are still needed to make the final decisions. The human decides which suggestions or insights to act upon. Nevertheless, the prospect of automatic filtering and sorting is highly beneficial in maneuvering the fragmented channel and campaign landscape taking place in practical online marketing work.

Practical guidelines:

  • As each vertical has its own KPIs, metrics and questions, there is a requirement of using many tools. For example, search-engine optimizers require drastically different information than display advertisers, and therefore it makes no sense to create a single solution. Instead, an organization should derive the tools from its business objectives and based on the specific information needed to achieve them.
  • An example of fine-grained automatic analytics is TenScores that only specializes on monitoring one metric in one channel (Quality Score in Google AdWords). Their approach makes sense because Quality Score is such an important metric for keyword advertisers and its optimization involves a complexity, enabling TenScores to provide in-depth recommendations that are valuable to end users.

However, even though the tools may be channel-specific, their operating principles can be similar. For example, stream filtering and anomaly detection algorithms are generalizable to many types of data, and thus have wide applicability. Moreover,

  • setting the frequency threshold to pinging decision makers is a key issue that should be experimented with when designing automatic analytics systems.

Even if there is automation, it is too early to speak of real artificial intelligence. The current systems always have manually set parameters and thresholds, and miss important things that are clear for individuals. For example, the previously shown Quill Engage cannot provide an explanation why the sales dropped when going from December to January — yet, this is apparent to any individual working in the gift business: Christmas season was the reason.

Implications for developers of automatic analytics systems: Developers of various analytics systems should no longer expect that their users log in to the system to browse reports. Instead, the critical information needs to be automatically mined and sent to decision makers in an actionable format (cf. SMART principle). There is already a considerable shift in the industry to this direction which will only be emphasized as customers realize the benefits of automatic analytics. Thus, we believe the future of analytics is more about detecting anomalies and opportunities, and giving decision makers easy choices to act upon. Of course, there are also new concerns in this environment, such as biased recommendations by online ad platforms – is the system advising you to increase bids because it maximizes your profit or because it increases the owner’s revenue?

Conclusion: Analytics software providers are planning to move toward the direction of providing automated insights, and researchers should follow suite. Open questions are many, especially relating to users’ interaction with automatic analytics insights: how responsive are users to the provided recommendation? What information do the users require? What actions do users take based on the information? We expect interesting studies in this field in the near future.

References

Google. (2017). Introducing Smart display campaigns. Retrieved February 12, 2018, from https://adwords.googleblog.com/2017/04/introducing-smart-display-campaigns.html

Jansen, B. J., & Schuster, S. (2011). Bidding on the buying funnel for sponsored search and keyword advertising. Journal of Electronic Commerce Research, 12(1), 1–18.

Lee, K.-C., Jalali, A., & Dasdan, A. (2013). Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising. In Proceedings of the Seventh International Workshop on Data Mining for Online Advertising (p. 1:1–1:9). New York, NY, USA: ACM. https://doi.org/10.1145/2501040.2501979

Salminen, J. (2009). Power of Google: A study on online advertising exchange. Turku: Master’s thesis. Turku School of Economics.

Shahin, A., & Mahbod, M. A. (2007). Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting. International Journal of Productivity and Performance Management, 56(3), 226–240.

Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

How to write emails that get read? 11 tips I use daily.

Here are some tips to make people more likely to read your email.

I’ve noticed that some people struggle to communicate effectively via email, so maybe sharing these tips will help someone.

Tips:

1. include *one message* per email — when you include 2 or more, the others easily get ignored. It’s better to send a new message, like “ps. one more thing…”

2. don’t make people think why you move from A to B, but make it evident from the text. Like, make a logical argument that explains itself. Find supporting evidence when needed and be truthful to yourself.

3. use short sentences, short paragraphs — people are scanning so shortness sells.

4. use plain words, don’t make people think

5. use words and phrases that cannot be misunderstood

6. be personal, use people’s names to catch their attention

7. use bolding and lists to facilitate scanning — in text-only, use *asterisk symbols* to emphasize

8. include the next steps — too many emails end up in a limbo, like what should I do after reading it?

Moreover,

9. do the thinking for the reader, so it’s easy to take action. Sometimes this means writing a single email can take an hour or more.

10. include all the relevant people when forwarding or replying — maximum transparency, maximum information

11. however, when you want a specific response, send your message individually. For example, don’t send survey links as mass-posting; approach people personally.

Got more tips? Share!

Medieval Game of Death (#bisnesidea)

korttipeli, jossa yritetään selvitä keskiajan kylän läpi.

A

plague – a rat bites you and infests you with plague
death
guillotine
alchemist
mermaid
hellhound
inquisition
witchcraft
burning at stake
lonely knight
harlequin

B

map:
back alley
market place
castle
tavern
underworld
gates of heaven

A+B –> jokaisessa oma tarina

“travel through a medieval town and see if you can survive”

googleta: card game mechanics

1. throw a dice
2. take a card
3. ???

vrt. tallinn legends
–> samantyylinen kaveri vetämään videon äänispiikin